Machine Learning Book Classification

How to use Python Pandas for loading dataset

Creating the model in Supervised machine learning

Use pickle to dump the model and vectorizer in the disk

Deploy machine learning model on Django

Requirements

  • Python, Django And Machine Learning Basics

Description

Become Artificial Intelligence Engineer.

This is a step-by-step course on how to create book classification using machine learning. It covers Numpy, Pandas, Matplotlib, Scikit learns, and Django, and at the end predictive model is deployed on Django. Most of the things machine learning beginners do not know is how they can deploy a created model. How to put created model into the application? The training model and get 80%, 85%, or 90% accuracy does not matter. As Artificial Intelligence Engineer you should be able to put created model into the application.

Actually, learning how to deploy a Machine Learning model created by machine learning is a big win for you and is a motivating effect towards improving, embracing, and learning machine learning. The piece me off when I hear people saying Artificial Intelligence is not really. It is just a theoretical study. Let’s learn together how to deploy models, solve people’s problems and change people’s minds about Artificial Intelligence.

At the end of this course, you will become Artificial Intelligence by your ability to put created models into the application and solve people’s problems. Not only that you will be exposed to a few concepts of Django which are Python web framework and current trending web framework. By understanding Django, you will be able to deploy the previously created model you could not in the previous time.

Who this course is for:

  • Python Developers interested with machine learning

Course content

Essentials of Machine Learning

An overview of the workflow from starting to launching an ML project

Essential terms that will pop up often during ML conversations

Overview of classification and regression goals

Understanding of some of the techniques you can use to optimize your ML model

Requirements

  • Curiosity about the machine learning project structure

Description

Machine Learning has become an exciting route to go down by many teams and companies. However, it’s not always realistic that everyone is expected to catch up with all of the latest ML trends.

Usually Machine learning teams are made up of different people. On the technical side you can have a mixture of the different data scientists and engineers, like a Machine Learning Data Scientists, as well as Machine Learning and Data Engineers. The data scientists’ main responsibility would be building out or improving the models, and the engineers will help with everything else around deployment and that the models are getting the data they need.

From the non-technical side it’s likely you’ll have a project manager and possibly also several other business stakeholders. This course is aimed for these people, who need to understand what’s going on at a higher level, without necessarily having to dive into the technical components. Those that need to know enough to help with product vision, and be able to have and understand discussions about current statuses, blockers, as well as estimations.

In this course we’ll look at some of the different components involved in an ML project so that you can feel like you can have fruitful conversations when working on an ML project without needing to get bogged up on all the technical details.

Who this course is for:

  • Anyone who wants to get a high-level overview of the different components involved in machine learning

Course content

Mastering Machine Learning: Course-1

Machine Learning

Python

Regression

Classification

Unsupervised Machine Learning

Requirements

  • Zero prior technical experience is required! All you need is a passion to learn and experiment new things.

Description

This course will be a part of series of Free ML Courses to become an expert of ML. Presenting here its First Course on Machine Learning for becoming expert of ML.

This course presents the concepts of Supervised Machine Learning, Unsupervised Machine Learning, Regression and Classification.

It covers implementation of Simple Linear Regression.

Who this course is for:

  • Machine Learning Enthusiast
  • Students taking Machine Learning Course
  • Professionals working in the area of Data Analytics
  • Students preparing for placement tests and interviews
  • Excellent course for all the students of Non-IT Branches as it provides the basic knowledge of ML without any prerequisite

Course content

CatBoost vs XGBoost – Quick Intro and Modeling Basics

Learn how to use CatBoost in regression and classification with Python

Requirements

  • Some Python and Modeling experience/interest

Description

XGBoost is one of the most powerful boosted models in existence until now… here comes CatBoost. Let’s explore how it compares to XGBoost using Python and also explore CatBoost on both a classification dataset and a regression one. Let’s have some fun!

Part 1

We’re going to start by unleashing XGBoost and CatBoostost on an independent data set version of the Titanic – the ship’s manifest of those that did and didn’t survive the tragic sinking of the ship in the North Atlantic Ocean. It happened in 1912 after hitting an iceberg on its maiden voyage to New York. You probably have already used it as it is extremely predictive, basically, women, children and the rich survived while men and the poor mostly didn’t.

Part 2

In the second part, we’ll model a linear regression and classification on the titanic for classification and the Boston housing data.I’ll also introduce you to a cool tool – Pandas Profiler for quick EDAs.

Please go out and use this model on a Kaggle competition, get an account if you haven’t already and experiment – sometimes follow the rules, sometimes, don’t. Remember that data science is very new so we’re still inventing things as we go, just like these new models allow us to explore a little further and further each time!

Who this course is for:

  • Beginning data scientists

Course content

Top 10 Machine Learning Algorithms

It is undeniably, machine learning and artificial intelligence have become immensely notorious over the past few years. Also, at the moment, big data is gaining notoriety in the tech industry where machine learning is amazingly powerful for delivering predictions or forecasting recommendations, relied on the huge amount of data. 

What are Machine Learning Algorithms?

Being a subset of Artificial Intelligence, Machine Learning is the technique that trains computers/systems to work independently, without being programmed explicitly.

And, during this process of training and learning, various algorithms come into the picture, that helps such systems in order to train themselves in a superior way with time, are referred as Machine Learning Algorithms. 



Machine learning algorithms work on the concept of three ubiquitous learning models:  supervised learning, unsupervised learning, and reinforcement learning. These are essentially the types of machine learning;

  • Supervised learning is deployed in cases where a label data is available for specific datasets and identifies patterns within values labels assigned to data points. 
  • Unsupervised learning is implemented in cases where the difficulty is to determine implicit connections in a given unlabeled dataset.
  • Reinforcement learning selects an action, relied on each data point and after that learn how good the action was.

(Related blog: Fundamentals to Reinforcement Learning)

Machine Learning Algorithm

In the intense dynamic time, several machine learning algorithms have been developed in order to solve real-world problems; they are extremely automated and self-correcting as embracing the potential of improving over time while exploiting growing amounts of data and demanding minimal human intervention.

Let’s learn about some of the fascinating machine learning algorithms;



  1. Decision Tree

The decision tree is the decision supporting tool that practices a tree-like graph or model of decisions along with their feasible outcomes, like the chance-event outcome, resource costs and implementation.

Being a supervised learning algorithm, decision trees are the best choice for classifying both categorical and continuous dependent variables. In this algorithm, the population is split into two or more homogeneous datasets, relying on the most significant characteristics or independent variables.



Over the graphical representation of the decision tree, the internal node highlights a test on the attribute, each individual branch denotes the outcome of the test, and leaf node signifies a specific class label, therefore the decision is made after computing all the attributes.

Fundamentally, decision trees are of two types;

  • Classification Trees– Accounted as the default kind of decision trees, classification trees are adapted to distinct the dataset into various classes on the basis of the response variable, and preferred when the response variable is categorical in nature.
  • Regression Trees– In opposite to the classification tree, regression trees are chosen when the response or target variable is continuous or numerical in nature and adapted in predictive based problems.
  1. Naive Bayes Classifier

A Naive Bayes classifier believes that the appearance of a selective feature in a class is irrelevant to the appearance of any other feature. It considers all the properties independent while calculating the probability of a particular outcome, even if each feature are related to each other.

The model involves two types of probabilities 

  • Probability of each class, and
  • Conditional Probability for each class, given each x value.

Both the probabilities can be measured directly from training data, once calculated, the probability model can be deployed for making predictions for new data via Bayes Theorem.

Some of the real-world cases of naive Bayes classifiers are to label an email as spam or not, to categorize a new article in technology, politics or sports group, to identify a text stating positive or negative emotions, and in face and voice recognition software.

  1. Ordinary Least Square Regression

Under statistics computation, Least Square is the method to perform linear regression. In order to establish the connection between a dependent variable and an independent variable, the ordinary least squares method is like- draw a straight line, later for each data point, calculate the vertical distance amidst the point and the line and summed these up.



The fitted line would be the one where the sum of distances is as small as possible. Least squares are referring to the sort of errors metric that are minimized.

  1. Linear Regression

Linear Regression describes the impact on the dependent variable while the independent variable gets altered, as a consequence an independent variable is known as the explanatory variable whereas the dependent variable is named as the factor of interest.

It shows the connection amid an independent and a dependent variable and deals with prediction/estimations in continuous values. E.g. it can be used for risk assessment in the insurance domain, to identify the number of applications for multiple ages users.

The linear regression can be described in terms of a best fit relationship among the input variable (x) and output variable (y) through identifying the specific weights for the input variables, named as coefficients (B), that is

y= B0 + B1*x



  1. Logistic Regression

Logistic regression is a powerful statistical tool of modelling a binomial outcome with one or more explanatory variables. It computes the association amid the categorical dependent variable and one or more independent variables through measuring probabilities by a logistic function (or cumulative logistics distribution). 

The Logistic Regression Algorithm works for discrete values, it is well suitable for binary classification where if an event occurs successfully, it is classified as 1, and 0, if not. Therefore, the probability of occurring of a specific event is estimated in the basis of provided predictor variables.



It has the real world applications as;

  • Credit scoring 
  • Estimating success rates of marketing campaigns
  • Anticipating the revenues generated by a certain product or service.
  • In politics, whether a particular candidate wins or loses the election. 
  1. Support Vector Machines

In SVM, a hyperplane (a line that divides the input variable space) is selected to separate appropriately the data points across input variables space by their respective class either 0 or 1.

Basically, the SVM algorithm determines the coefficients that yield in the suitable separation of the various classes through the hyperplane, where the distance amid the hyperplane and the closest data points is referred to as the margin. However, the optimal hyperplane, that can depart the two classes, is the line that holds the largest margin.



Only such points are applicable in determining the hyperplane and the construction of the classifier and are termed as the support vectors as they support or define the hyperplane. 

More specifically, SVM renders appropriate classification for classification problems upon the training data, and extra efficiency for accurate classification of the future and also doesn’t overfit the data.

SVM is greatly used for stock market forecasting, for instance, one can use it to check the relative performance of the one stocks when compared to the other stocks under the same market. Thereby, with the help of the relative comparison of stocks, one can manage investment and make decisions depending upon the classifications made by SVM algorithm. 

  1. Clustering Algorithms

Being an unsupervised learning problem, clustering approach is used as a data analysis technique for identifying informative data patterns, such as groups of customers based on their behavior or locations. Clustering descibes a class of problem and a class of methods, take a look at  Clustering Methods and Applications.

Clustering Algorithms refer to the group task of clustering, i.e. grouping an assemblage of objects in such a way that each object is more identical to each other under the same group (cluster) in comparison to those in separate groups.

There are various kinds of clustering algorithms that use similarity or distance measures amid examples in the feature space to find out dense regions of observations. Therefore, it is a good attempt to scale data previously for using clustering algorithms. However, each clustering algorithm is different, some of them are connectivity-based algorithms, dimensionality reduction, neural networks, probabilistic, etc. 

  1. Gradient Boosting & AdaBoost

Boosting algorithms are used while dealing with massive quantities of data for making a prediction with great accuracy. It is an ensemble learning algorithm that integrates the predictive potential of diversified base estimators in order to enhance robustness, i.e. it blends the various vulnerable and mediocre predictors for developing strong predictor/estimator.

In simple terms, Boosting is the learning algorithm that makes an active classifier/predictor from a weak or average classifier. This process is achieved through creating a model from training data, and then constructing another second model in order to correct the errors from the first  model. Also, until the training set is precisely predicted, models are added or until the maximum number of models are joined.

These algorithms usually fit well in data science competitions like Kaggle, Hackathons, etc. As treated most preferred ML algorithms, these can be used with Python and R programming for obtaining accurate outcomes.

AdaBoost was the first competitive boosting algorithm that was constructed for binary classification. It can be considered as the initial step for learning and understanding boosting. Most of the modern boosting methods are constructed over AdaBoost, preferably on stochastic gradient boosting machines. 

The video below explains the AdaBoost which is just the simple twist on decision trees and random forests.



  1. Principal Component Analysis

Dimensionality reduction algorithms are among the most important algorithms in machine learning that can be used when a data has multiple dimensions.

Consider a dataset that has “n” dimension, for instance a data professionlist is working on financial data that has the attributes as a credit score, personal details, personnel salary, etc. Now, in order to understand the important labels for building a required model, he/she can use dimensionality reduction method, and PCA is the appropriate algorithm for reducing dimensions.

PCA is a statistical approach that deploys an orthogonal transformation for reforming an array of observations of likely correlated variables into a set of observations of linearly uncorrelated variables, is known as principal components.

Using PCA, one can decrease the quantity of dimensions while retaining the important labels of the model. Since PCA is heavily relied on the number of dimensions, each PCA is perpendicular to the other and their dot product is zero.



Its applications include analysing data for smooth learning, and visualization. Since all the components of PCA have a very high variance, it is not an appropriate approach for noisy data.

  1. Deep Learning Algorithms

Deep learning algorithms heavily rely on the nervous system of a human and are generally designed on the neural networks that use plentiful computational resources. All these algorithms use different types of neural networks to perform particular tasks.

They train computers by learning from examples and industries such as healthcare, eCommerce, entertainment, and advertising commonly use deep learning algorithms. 

Since, the deep learning algorithms signify self-learning representations, they basically coin ton ANNs that reflect the way a human brain functions and computes information. While the training process starts, algorithms intakes unknown components as the input data for extracting out features, group objects, and find out useful and hidden data patterns. 

These algorithms make use of several algorithms where no one single network is considered perfect and some algorithms are perfect fitted to perform specific tasks. However, in order to choose the correct ones, one should have a strong understanding of all primary algorithms

Some popular deep learning algorithms are Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), etc.

Conclusion


From the above discussion, it can be concluded that Machine learning algorithms are programs/ models that learn from data and improve from experience regardless of the intervention of human being.

Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal. – Eric Schmidt (Google Chairman)

Some popular examples, where ML algorithms are being deployed, are Netflix’s algorithms that recommend movies based on the movies (or movie genre) we have watched in past, or Amazon’s algorithms that suggest an item, based on review or purchase history. 

What Are the Major Limitations of Machine Learning Algorithms?

Table of Contents

015 key limitations of machine learning algorithms

02When is a machine learning application not the best choice?

03With all its limitations, is ML worth using?

Over the past few years, artificial intelligence (AI) and machine learning (ML) developers have made AI and ML think more like humans, performing complex tasks and making decisions based on deep analysis. Robots performing various jobs for humans are no longer the plot of science fiction films, but the reality of today. However, despite the progress data scientist teams have made in this field, there are still several limitations of machine learning algorithms.

The number of AI consulting agencies has skyrocketed over the past few years, accompanied by a 100% increase in AI-related jobs between 2015 and 2018. This boom has fueled the growth of ML in all kinds of industries.

While ML is very useful for many projects, sometimes it’s not the best solution. In some cases, ML implementation is not necessary, does not make sense, and can even cause more problems than it solves. This article discusses instances where ML is not an appropriate solution.

Want to know more about tech trends?

5 key limitations of machine learning algorithms

ML has profoundly impacted the world. We are slowly evolving towards a philosophy that Yuval Noah Harari calls “dataism”, which means that people trust data and algorithms more than their personal beliefs.

If you think this definitely couldn’t happen to you, consider taking a vacation in an unfamiliar country. Let’s say you are in Zanzibar for the first time. To reach your destination, you follow the GPS instructions rather than reading a map yourself. In some instances, people have plunged full speed into swamps or lakes because they followed a navigation device’s instructions and never once looked at a map.

ML offers an innovative approach to project development that requires processing a large amount of data. But what key issues should you consider before you choose ML as a tool to develop for your startup or business? Before implementing this powerful technology, you must be aware of its potential limitations and pitfalls. ML issues that may arise can be classified into five main categories, which we highlight below.

What Are the Major Limitations of Machine Learning Algorithms? - photo 1

Ethical concerns

There are, of course, many advantages to trusting algorithms. Humanity has benefited from relying on computer algorithms to automate processes, analyze large amounts of data, and make complex decisions. However, trusting algorithms has its drawbacks. Algorithms can be subject to bias at any level of development. And since algorithms are developed and trained by humans, it’s nearly impossible to eliminate bias.

Many ethical questions still remain unanswered. For example, who is to blame if something goes wrong? Let’s take the most obvious example — self-driving cars. Who should be held accountable in the event of a traffic accident? The driver, the car manufacturer, or the developer of the software?

One thing is clear — ML cannot make difficult ethical or moral decisions on its own. In the not too distant future, we will have to create a framework to solve ethical concerns about ML technology.

Deterministic problems

ML is a powerful technology well suited for many domains, including weather forecasting and climate and atmospheric research. ML models can be used to help calibrate and correct sensors that allow you to adjust the operation of sensors that measure environmental indicators like temperature, pressure, and humidity.

Models can be programmed, for example, to simulate weather and emissions into the atmosphere to forecast pollution. Depending on the amount of data and the complexity of the model, this can be computationally intensive and take up to a month.

Can humans use ML for weather forecasting? Maybe. Experts can use data from satellites and weather stations along with a rudimentary forecasting algorithm. They can provide the necessary data like air pressure in a specific area, the humidity level in the air, wind speed, etcetera, to train a neural network to predict tomorrow’s weather.

However, neural networks do not understand the physics of a weather system, nor do not understand its laws. For example, ML can make predictions, but the calculations of such intermediate fields as density can have negative values that are impossible under the laws of physics. AI does not recognize cause-and-effect relationships. The neural network finds a connection between input and output data but cannot explain the reason they are connected.

Lack of Data

Neural networks are complex architectures and require enormous amounts of training data to produce viable results. As the size of a neural network’s architecture grows, so does its data requirement. In such cases, some may decide to reuse the data, but this will never bring good results.

Another problem is related to the lack of quality data. This is not the same as simply not having data. Let’s say your neural network requires more data, and you give it a sufficient quantity, but you give it poor quality data. This can significantly reduce the model’s accuracy.

For example, suppose the data used to train an algorithm to detect breast cancer uses mammograms primarily from white women. In that case, the model trained on this dataset might be biased in a way that produces inaccurate predictions when it reads mammograms of Black women. Black women are already 42% more likely to die from breast cancer due to many factors, and poorly trained cancer-detection algorithms will only widen that gap.

Lack of interpretability

One significant problem with deep learning algorithms is interpretability. Let’s say you work for a financial firm, and you need to build a model to detect fraudulent transactions. In this case, your model should be able to justify how it classifies transactions. A deep learning algorithm may have good accuracy and responsiveness for this task but may not validate its solutions.

Or maybe you work for an AI consulting firm. You want to offer your services to a client that uses only traditional statistical methods. AI models can be powerless if they cannot be interpreted, and the process of human interpretation involves nuances that go far beyond technical skill. If you can’t convince your client that you understand how an algorithm comes to a decision, how likely is it that they will trust you and your experience?

It is paramount that ML methods achieve interpretability if they are to be applied in practice.

Lack of reproducibility

Lack of reproducibility in ML is a complex and growing issue exacerbated by a lack of code transparency and model testing methodologies. Research labs develop new models that can be quickly deployed in real-world applications. However, even if the models are developed to take into account the latest research advances, they may not work in real cases.

Reproducibility can help different industries and professionals implement the same model and discover solutions to problems faster. Lack of reproducibility can affect safety, reliability, and the detection of bias.

When is a machine learning application not the best choice?

Nine times out of ten ML should not be applied with no labeled data and personal experience. Almost always labeled data is essential for deep learning models. Data labeling is the process of marking up already “clean” data and organizing it for machine learning. If you do not have enough high-quality labeled data, the use of ML is not recommended.

Another example of when to avoid AI is in designing mission-critical security systems because ML requires more complex data than other technologies.

The more data needs to be processed, the greater the complexity and vulnerability. This includes aircraft flight controls, nuclear power plant controls, and so on.

With all its limitations, is ML worth using?

It cannot be denied that AI has opened up many promising opportunities for humanity. However, it’s also led some to philosophize that machine learning algorithms can solve all of humanity’s problems.

Machine learning systems work best when applied to a task that a human would otherwise do. It can do well if it isn’t asked to be creative, intuitive, or use common sense.

Algorithms learn well from explicit data, but it doesn’t understand the world and how it works the way we humans do. For example, an ML system can be taught what a cup looks like, but it doesn’t understand that there is coffee in it.

People feel these limitations, like common sense and intuition, when they interact with AI. For example, chatbots and voice assistants often fail when asked reasonable questions that involve intuition. Autonomous systems have blind spots and fail to detect potentially critical stimuli that a person would immediately notice.

The power of machine learning helps people do their jobs more efficiently and live better lives, but it cannot replace them because it cannot adequately perform many tasks. ML offers certain advantages but also some challenges. 

At Postindustria, we are skilled in overcoming the limitations and have extensive experience in ML development. We are ready to take on your project. Leave us your contact details and we will reach out to discuss your solution.

Machine Learning Algorithms: Mathematical Deep DiveRead

Types of Learning Styles for Machine Learning Algorithms

Before we begin discussing specific algorithms, however, we need a basic understanding of the different learning styles often seen in machine learning algorithms. These learning styles are supervised, unsupervised, and semi-supervised learning.

Types of Machine Learning and Differences Between Learning Styles

Supervised Learning

Supervised learning is a technique where a task is accomplished by providing training (input and output) patterns to the systems. Supervised learning in machine learning maps an input to its outputs based on sample input-output pairs. This type of learning infers a function from labeled and organized training data that consists of sets of training examples.

Datasets used for training with supervised learning are labeled with X and Y-axis variables and usually consist of metadata rather than image inputs. A popular example of a supervised learning dataset is the University of California Irvine Iris Dataset where the predicted attribute is the class of Iris (a type of flower) and the inputs are different variables depicting Iris classes (petal width and length, sepal width and length).

Supervised learning models are prepared through a training process where the model is required to make predictions and corrects itself when those predictions are incorrect. This guess-and-check type process continues until the model achieves a predetermined level of accuracy using the given training data.

Algorithms used for supervised learning are logistic regression and back propagation neural networks. Machine learning problems regarding supervised learning often involve classification and regression.

Unsupervised Learning

Unsupervised learning for machine learning does not require the creator to “supervise” the model during training. Unsupervised learning allows the model to discover patterns independently of a well-organized and labeled dataset. This is why unlabeled datasets are used often for unsupervised learning to generate machine learning models.

Unsupervised learning leaves algorithms to find structure in the inputs without the use of predefined variable names or labels.

While supervised learning models are given the inputs and left to predict the outputs, unsupervised learning models predict both the inputs and the outputs. This is done by detecting prominent patterns between inputs and associating those with potential outputs.

Algorithms specific to unsupervised machine learning models include K-means and the Apriori algorithm. Popular machine learning problems that can be solved using unsupervised learning involve clustering, dimensionality reduction, and association rule learning.

Semi-supervised Learning

Semi-supervised learning is the middle ground between unsupervised and supervised learning. Semi-supervised learning usually requires a combination of a small amount of labeled data and a comparatively large amount of unlabeled data for training its machine learning models.

As with supervised learning, example machine learning problems that can be solved with semi-supervised learning include classification and regressions. Semi-supervised learning is useful for image classification purposes but is still debated on whether it is the most efficient for that purpose.

Regression Algorithms for Machine Learning

Algorithms allow for sectors of machine learning like unsupervised, supervised, and semi-supervised learning to be productive.

Regression algorithms can be used for supervised and semi-supervised learning, and specifically deal with modeling the relationship between variables. It is refined iteratively using a measure of error in the predictions made by the model at hand. Regression is both a type of machine learning problem and algorithm.

For our intents and purposes, we are discussing regression as it relates to being a type of error-based machine learning algorithm. Popular regression algorithms include a handful, but the most relevant to us in this article are linear, logistic, and ordinary least squares regression.

Simple Linear Regression for Machine Learning

Regression is a method of modeling a target value based on independent predictors. This makes regression a good method for modeling a target value based on independent predictor variables.

Linear regression for machine learning algorithms Simple linear regression is a relatively easy concept for a machine learning model algorithm in part due to its simplicity. The number of independent variables is 1, and there is a linear relationship between the independent and dependent X and Y variables. The line of best fit models given points in a dataset with the most accuracy. The line is then modeled with a linear equation where y = A0 + A1(x) → or y = mx + b.

Error Function

The error function (also known as “cost function”)  is used to provide the “best” line of best fit for our respective data points. The search problem is converted into a minimization problem where the error between the predicted value and actual value is the least. In other words, the error function captures the error between the predictions made by a model versus the actual values in a training dataset.

Linear regression as an algorithm aims to find the best fitting values for A0 and A1 which is a two-part process that can be better understood by learning about cost function and gradient descent.

Sum of Squared Errors Error Function

The above mathematical representation is the formal definition of the sum of squared errors error function. Keep in mind that M is the complete machine learning model at hand, instead of a variable that represents a single value.

The training set given is composed of n training instances, each of which have d descriptive features and T single-target feature. Mw (d) is the prediction made by the candidate model at hand for any given training instance containing di descriptive features. The machine learning aspect of this function can be seen where the candidate model Mw  is defined by the weight vector w.

If we take, for example, a scenario where each instance is described with a single descriptive feature (meaning each independent data point maps to a singular dependent data point as the descriptive feature), the equation can expand to something like the following:

Expanded Error Function

The weights can change, there is a corresponding sum of squared errors value for every possible combination of weights (weights are also known as model parameters). The key to using simple linear regression models is determining the optimal values for the weights using the equation.

Error Surfaces to Find Optimal Values

Error Surfaces to Determine Optimal Values The sum of squared errors function can be used to measure how well any combination of weights fits the respective instances in a dataset. Instead of using a brute-force search to find the best combination, we can use associated error surfaces to calculate the optimal combination of weights.

The error surfaces for any machine learning simple regression problem are convex (shaped like a bowl). We want to use the error surfaces to find a unique set of optimal weights with the lowest sum of squared errors (finding the global minimum). Finding the global minimum uses a process known as least squares optimization.

Least Squares Optimization

As mentioned earlier, we can predict that the error surface of our algorithm will produce a convex shape, thus possessing a global minimum. The distinctive shape of the error surfaces can be attributed to it being defined mostly by the linearity of the model, rather than the properties of the data. This means the partial derivatives of the error surface with respect to weights (w[0] and w[1]) are equal to 0 because they measure the slope of the error surface at the points w[0] and w[1].

The bottom of the error surface is not curved and does not have any slope, due to the convex shape, which is why the partial derivatives of the error surface are 0 at that point. We can infer that because of the aforementioned properties, that point is the global minimum of the error surface and are exactly the points we would need to calculate the most efficient weights to use for machine learning.

So how do we calculate the global minimum? By combining the equation discussed earlier for simple linear regression with partial derivatives, we can define the global minimum on the error surface as:

Global Minimum on the Error Surface

Multivariable Linear Regression with Gradient Descent

Multivariable linear regression with gradient descent can be used to train a best-fit model for a given training set and is one of the most common approaches to doing so within error-based machine learning.

It is still used only for supervised and semi-supervised learning because a dataset is required. While the simple linear regression section above dealt with a single descriptive feature, multivariable linear regression models can handle more than one descriptive features (and are thus, multivariable).

A random example of a machine learning model where this type of algorithm would be useful is for predicting the price of a house in a neighborhood where variables like land size, crime rate, and miles from schooling are integral to the fluctuating price of the house.

Multivariable Linear Regression Mathematical Representation

We can actually take our simple linear regression representation from earlier in the article and extend it to include an array of variables rather than just one.

variables extended simple linear regression representation

In this instance, d is the array of m descriptive features. Thus, d[1]…d[m] represents all descriptive features, while w[1]…w[m] are m+1 weights. We can then simplify the above expansion by using a nonexistent descriptive feature, d[0], which is equal to 1.  This then produces:

expanded simple linear regression representation

We can easily spot the summation process in this equation, so we can replace it with sigma, and reduce the sum of all descriptive features to just Mw(d) =w*d.

What this means, in essence, is that w*d is the sum of the products of the arrays’ w and d corresponding elements. The loss function calculated earlier (L2) can be altered to reflect the new regression equation which takes into account multi variability.

Summation Process Replaced

What this means, in essence, is that w*d is the sum of the products of the arrays’ w and d corresponding elements. The loss function calculated earlier (L2) can be altered to reflect the new regression equation which takes into account multi variability.

Multivariable Model Accounting for Dot Product

Here, w*d is representative of the dot product of the vectors w and d, as deduced earlier. The above multivariable model allows us to include more than one descriptive feature in a concise way. An example of the equation using real-life variables (referring to our house-price example) would be: price = w[0] +w[1] * landSize + w[2] *crimeRate

Gradient Descent as a Concept

Finding the best-fit set of weights for machine learning with multivariable regression is done using a process called gradient descent. The previous global minimum approach is not as effective for multivariable regression as it is for simple linear regression because the number of instances in the training set and number of weights make the equation not computationally feasible.

We can still, however, use error surfaces as part of the gradient descent process to calculate the global minimum (which is different from simple linear regression). Instead of visualizing a simple, bowl-shaped error surface, imagine a non-convex error surface where there are peaks and valleys – similar to a mountainous landscape:

Set of weights for machine learning with a gradient descent method.

Gradient descent begins by selecting a random point within the space of weight. Our algorithm can only use local information from around this point because the rest of the space is unknown. Using the slope of the error surface at that location, the randomly selected weights can then be adjusted little by little in the direction of the error surface gradient to move to a new position on the error surface.

The adjustments are made in the direction of the error surface gradient, allowing for each new point to be slightly closer to the overall global minimum, until the global minimum is reached (where the slope is 0). The saddle point in the above image is the random point we start with. We then go to the local minimum and keep iterating the process until the global minimum is achieved.

Gradient Descent as an Algorithm (Batch Gradient Descent)

The gradient descent algorithm for training multivariable linear regression models requires a set of training instances (referred to as D), as well as a learning rate (α) for controlling the rate at which the algorithm converges, a convergence criterion that indicates when to stop the algorithm iterations, and lastly, an error delta function that determines the direction in which to adjust the randomized weight for coming closer to the global minimum.

Taking into consideration more than one variable changes the previous sum of squared errors for each training instance to:

Multivariable Sum of Squared Errors

Just as a reminder, D in this case refers to the set of training instances and w[j] (discussed later) is the randomly selected beginning weight.

Error Delta Function for Multivariable Linear Regression

For each calculated gradient using the above equation for machine learning algorithms, we want to move in the opposite direction for the purpose of returning a lower value on the error surface. The error delta function calculates the delta value that determines the direction and the magnitude of the adjustments made to each weight.

The delta value is determined by the gradient of the error surface at the latest position in the space of weight. Therefore, our error delta function (whose purpose is to calculate the direction to move in for a given weight), is:

The above is the weight update rule for multivariable linear regression with gradient descent, where tiis the expected target feature for i training instance and relates to w (the weight vector), di [j] (j^th descriptive feature corresponding to the i^th instance of training), and (the constant learning rate). The error delta function is the lower equation starting with the sigma sign (in curly brackets) within the greater weight update rule.

Error Delta Function

The above is the weight update rule for multivariable linear regression with gradient descent, where ti is the expected target feature for i training instance and relates to w (the weight vector), di [j] (j descriptive feature corresponding to the i instance of training), and (the constant learning rate). The error delta function is the lower equation starting with the sigma sign (in curly brackets) within the greater weight update rule.

Understanding the Weight Update Rule

Understanding the weight update rule for machine learning algorithms starts with understanding how it changes weights based on the error calculations by predictions made in the current candidate model.

If the errors show that predictions made by the model at hand are too high, then the weights should be decreased while di [j] is positive and increased while negative.

If the errors show that predictions made by the model at hand are too low, then the weights should be increased while di [j] is positive and increased while negative.

This process in its entirety is also known as “batch gradient descent” because one adjustment is made to each weight for each iteration, so the weights are changed in “batches.”

Logistic Regression

Logistic regression is useful when the dependent variable is “binary” in nature. This means logistic regression is usually associated with examining the association of independent variables with one dichotomous dependent variable. Meanwhile, linear regression is used for when the dependent variable is continuous, and the nature of the line of regression is linear.

A random example of how logistic regression has been used as an algorithm for machine learning is modeling how new words enter a language over time or how the amount of people using a product changes its buying patterns over time.

Logistic Regression Function

For logistic regression models, the output of the basic linear regression model is outputted using the logistic function. The logistic function is a threshold function that is continuous and differentiable.

Logistic Function

For reference, the above equation represents e as Euler’s number (2.71828), the base of the natural logarithms, and x as the input value.

Instead of the regression function being the dot product of the weights and descriptive features like in linear and multivariable regression functions, the logistic function passes the dot products of weights and descriptive feature values through it. The decision surface that results from the above equation once values are substituted is quite different from the error surfaces we saw earlier for multivariable and linear regression.

Decision Surface for Logistic Regression

The decision surface for logistic regression is not to be confused with the error surfaces discussed earlier – they are two separate ideas. The decision surface shows the value of the logistic equation for every possible input value. Logistic regression usually has a gentle transition from faulty target predictions to good target predictions.

This is a benefit for using logistic regression, since the correct and incorrect prediction accuracies are not as “black and white” as they are for simple linear and multivariable regression. Logistic regression models can also be interpreted as probabilities of the occurrence of a target level (through the use of the logistic function).

Decision Surface for Logistic Regression

Strategies for Improving Machine Learning Algorithms: Tips & Tricks

Machine learning and deep learning algorithms are all around us in modern businesses. The number of AI applications that may be used has been rapidly increasing with the rapid advancement of new algorithms, cheaper compute, and greater data availability. Every field, from banking to healthcare to education to manufacturing, construction, and beyond, has its own set of machine learning and deep learning solutions.

The biggest problem in all of these ML and DL projects across various sectors is model improvement. So, in this post, we’ll look at methods for improving machine learning models based on structured data (time-series, categorization) and deep learning models based on unstructured data (text, images, audio/video).

Importance of Data Structure

The first thing to understand before we get into strategies for machine learning modeling is to emphasize the importance of data i.e. “what kind of data do you have?”. This is important because ML requires a lot of data in order to train properly. This data must be organized in a way that is easy for the algorithm to understand and use. Data structures provide this organization, making machine learning possible. Without data structures, machine learning would be very difficult, if not impossible. Data must be carefully arranged so that the algorithm can learn from it effectively. Data structures provide this organization, allowing machine learning to take place.

As such, data can be classified into two categories:

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  • Structured Data — is easier to process and analyze than unstructured data. It’s usually arranged in a fixed format that makes it easy to extract specific pieces of information, which can be helpful for certain types of predictions. For example, if you’re trying to predict how if the price of stock will go up in the next month, you might find it helpful to use data that’s been formatted as a table or spreadsheet. This type of data works best with supervised learning models.
  • Unstructured Data — can be a valuable source of information for predictions in machine learning, because it can contain more diverse and nuanced information than structured data. For example, unstructured text data can include information about the sentiment or emotional state of a customer, which might be useful for predicting whether that customer is likely to churn. This type of data works best with unsupervised learning models.
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Table 1 — Structured & Unstructured Data Comparison

Machine Learning Algorithms Cheat Sheet

Information in this section provided by SAS Blog to be used for reference only.

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Source: SAS Blog — ML Cheat Sheet

How to use the cheat sheet

Read the path and algorithm labels on the chart as “If <path label> then use <algorithm>.” For example:

  • If you want to perform dimension reduction then use principal component analysis.
  • If you need a numeric prediction quickly, use decision trees or linear regression.
  • If you need a hierarchical result, use hierarchical clustering.

Sometimes more than one branch will apply, and other times none of them will be a perfect match. It’s important to remember these paths are intended to be rule-of-thumb recommendations, so some of the recommendations are not exact. Several data scientists I talked with said that the only sure way to find the very best algorithm is to try all of them.

Strategies for Improving ML Models — Structured Data

There are many methods for improving machine learning models based on structured data. Some of the most common methods include:

1.     Feature selection: Identifying and selecting the most relevant features from the data can help improve the accuracy of machine learning models. For example, selecting only the most important features from a dataset can help reduce overfitting and improve generalization.

2.     Feature engineering: This involves transforming or creating new features from existing ones to better capture relationships in the data. For instance, one could engineer features that capture quadratic or cubic relationships between variables in order to improve the predictive power of a machine learning model.

3.     Model selection and tuning: Trying out different machine learning models (e.g., linear regression, decision trees, random forests) and tuning their hyperparameters (e.g., regularization strength, tree depth) can help improve the performance of the final model.

4.     Data pre-processing: This step can involve various techniques such as imputation (filling in missing values), outlier removal, and normalization/standardization. Proper data pre-processing can improve the accuracy of machine learning models.

Strategies for Improving ML Models — Unstructured Data

There are various methods for improving machine learning models based on unstructured data. Some of these methods include the following:

1.     Using a pre-trained model: A pre-trained model is a machine learning model that has been trained on a large dataset, such as ImageNet. This type of model can be used to improve the performance of a machine learning model that is being trained on a smaller dataset.

2.     Using more data: The more data that is available to train a machine learning model, the better the model will perform. This is because more data provides more opportunities for the algorithm to learn from and identify patterns in the data.

3.     Training multiple models: Instead of training one single machine learning model, it can be beneficial to train multiple models. This is because each model can learn from different aspects of the data and improve the overall performance of the machine learning system.

4.     Ensembling: Ensembling is a technique that combines the predictions of multiple machine learning models to produce a more accurate prediction. This can be done by training multiple models on the same dataset and then taking the average of their predictions, or by training multiple models on different subsets of the data and then taking the majority vote of their predictions.

5.     Feature engineering: Feature engineering is the process of creating new features from existing data. This can be done by transforming existing features, such as using PCA to create new features from existing ones, or by creating new features from scratch, such as using the data from an accelerometer to create a new feature that represents the speed of the device.

6.     Model tuning: Model tuning is the process of adjusting the hyperparameters of a machine learning model to improve its performance. This can be done by using techniques such as grid search or random search.

7.     Regularization: Regularization is a technique that is used to prevent overfitting in machine learning models. This is done by adding constraints to the model, such as limiting the number of parameters that can be used, or by adding penalty terms to the objective function that are associated with large values of the parameters.

8.     Data augmentation: Data augmentation is a technique that is used to generate new data from existing data. This can be done by randomly perturbing the existing data, such as adding noise to images or changing the order of words in text documents.

9.     Transfer learning: Transfer learning is a technique that is used to learn from other tasks that are related to the task at hand. This can be done by pre-training a machine learning model on a large dataset and then fine-tuning it on the smaller dataset.

10. Dimensionality reduction: Dimensionality reduction is a technique that is used to reduce the number of features that are used to represent the data. The primary benefits of DR includes that it can help to simplify the data, making it easier to work with and understand, it can help to improve the results of machine learning algorithms by reducing the noise in the data and it can also reduce computational costs by reducing the number of features that need to be processed.

Strategies for Improving ML Models — Overall

There are many different ways to improve machine learning and deep learning models. Some common strategies include:

  • Using more data: This is often the most important factor in improving a model’s accuracy. The more data you can train your model on, the better it will perform.
  • Preprocessing the data: This can help improve the accuracy of your models by removing noise and spurious correlations from the data.
  • Manually tweaking the hyperparameters of your algorithms: This can help improve the performance of your models by optimizing them for your specific dataset and task.
  • Using ensembles of models: Combining multiple models into an ensemble can often lead to better performance than using a single model.
  • Normalization: Normalization is a technique used in machine learning to adjust the range of values in a dataset so that all values are within a certain range. This is often done to make sure that the data can be accurately processed by the machine learning algorithm. There are many different types of normalization, but usually it involves adjusting the data so that the mean value is zero and the standard deviation is one. This ensures that all values in the dataset are normalized within a range of -1 to 1.
  • Standardization: Standardization is a process of cleaning and preparing data so that it can be used in machine learning algorithms. This process involves rescaling variables so that they have a mean of 0 and a standard deviation of 1, which ensures that all the variables are in the same scale. Standardization is especially important when you are comparing different machine learning models, as it ensures that all the models are using the same data.
  • One-hot encoding: This technique transforms categorical variables into binary vectors. This is useful for datasets with features that are categorical (e.g., gender, race, etc.).
  • Understanding the errors: Machine learning models are only as good as the data they are trained on. If you don’t understand what kind of errors your AI model is making, you run the risk of perpetuating inaccurate information and biases. For example, if you have a machine learning model that is classifying images, and it is mistakenly classifying images of black people as gorillas, then you need to be aware of that error so you can fix it. Otherwise, your model will continue to incorrectly classify images, which could have serious implications for real-world applications.
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Source: Tech eBay — The six phases of ML modeling and their acceptance criteria

Normalization of Data

Normalization is a machine learning technique that helps to standardize data so that it can be better processed by algorithms. By normalizing data, we can reduce the amount of variability in our dataset, making it more predictable and easier to work with. There are several different techniques for normalizing data, but the most common methods involve rescaling data so that all values lie between 0 and 1, or standardizing data so that each value has a mean of 0 and a standard deviation of 1.

One reason why Normalization is important is because many machine learning algorithms assume that data is normally distributed (i.e. bell-shaped). This means that if our data is not normalized, then these algorithms may not work as well. In addition, normalizing data can help to improve the accuracy of some machine learning algorithms, and can make it easier to compare different datasets.

When to Normalize Data?

Normalization is a feature scaling technique that is used when the data have an unknown distribution or do not have a Gaussian Distribution. This method of data scaling is employed when the data has a broad scope and the algorithms that train the data do not make assumptions about how it will be distributed, such as with an Artificial Neural Network.

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Source: Analyst Answer

There are a few different ways to normalize data:

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Source: Somenka.net

1. Rescaling: This means that all values in the dataset are scaled so that they lie between 0 and 1. To rescale data, we first need to calculate the minimum and maximum values for each feature (column). We then subtract the minimum value from each value in the column, and divide by the range (maximum — minimum).

· Tip: rescaling is a good choice if you want to ensure that all values in your dataset are between 0 and 1.

2. Standardization: This technique transforms data so that it has a mean of 0 and a standard deviation of 1. Unlike rescaling, standardization does not necessarily bound values to a specific range. To standardize data, we first need to calculate the mean and standard deviation for each column. We then subtract the mean from each value in the column, and divide by the standard deviation.

· Tip: Standardization is a good choice if you want to center your data around 0, or if you want to make sure that all values have the same scale.

3. Min-Max Scaling: This is a type of rescaling that transforms data so that all values lie between 0 and 1. Unlike other methods of rescaling, min-max scaling does not center the data around 0. Instead, it scales the data such that the minimum value is 0 and the maximum value is 1. To min-max scale data, we first need to calculate the minimum and maximum values for each column. We then subtract the minimum value from each value in the column, and divide by the range (maximum — minimum).

· Tip: Min-Max Scaling is a good choice if you want to ensure that all values in your dataset are between 0 and 1, but you don’t necessarily want to center the data around 0.

4. Principal Component Analysis (PCA): This is a technique that can be used to reduce the dimensionality of data. It does this by creating new, artificial features that are linear combinations of the original features. These new features are called principal components, and they are ranked in order of importance. The first principal component is the one that explains the most variance in the data, and each subsequent component explains less and less variance. To use PCA to normalize data, we first need to calculate the principle components for our dataset. We then subtract the mean from each value in each column, and divide by the standard deviation.

· Tip: PCA is a good choice if you want to reduce the dimensionality of your data

5. Z-Score Scaling: This is a type of standardization that transforms data so that it has a mean of 0 and a standard deviation of 1. To z-score scale data, we first need to calculate the mean and standard deviation for each column. We then subtract the mean from each value in the column, and divide by the standard deviation.

· Tip: Z-Score Scaling is a good choice if you want to standardize your data without having to calculate the mean and standard deviation for each column.

The method you choose will depend on your dataset and what you want to achieve with it. Whichever method you choose, it’s important to remember that normalizing data is an important step in preprocessing data for machine learning. Without normalization, some machine learning algorithms may not work as well, and it may be more difficult to compare different datasets.

Best Practices for ML Algorithms

The best practices for using machine learning algorithms vary depending on the problem you’re trying to solve. However, some general best practices include:

  1. Choose the right algorithm: Choosing the right algorithm for your data is important, as it can affect the results that you get. Three of the most common ML algorithms are linear regression, decision trees, and Naive Bayes. For example, linear regression is good for predicting values based on a set of known inputs, while clustering is good for grouping data into clusters.
  2. Data preparation: This is one of the most important aspects of machine learning (ML). Without clean and feature-rich data, it is very difficult to train accurate ML models. Data preparation includes tasks such as identifying and dealing with outliers, filling in missing values, creating new features from existing data, etc. All of these tasks require a deep understanding of the data and the ML algorithms that will be used to train the model. Every machine learning algorithm has different requirements for the input data. For example, some algorithms can deal with missing values better than others. Some can work with categorical data while others require numerical data. So, it is important to select the right algorithms for your data and prepare the data accordingly.
  3. Preprocess your data: By preprocessing your data, you can ensure that your algorithm is working with clean and consistent data. This can drastically improve the performance of your algorithm. Additionally, preprocessing your data can help to reduce noise and remove outliers. This can again improve the performance of your machine learning algorithm
  4. Train your model carefully: Don’t overfit your data; choose an appropriate number of layers and parameters for your model, and use cross-validation to test its accuracy.
  5. Evaluate your results: Always evaluate your results to see how well your machine learning algorithm is performing. This will help you fine-tune your algorithms and ensure they’re working as effectively as possible.
  6. Tune your model: Once you’ve chosen and configured your algorithm, you need to tune it for optimal performance. This includes finding the right combination of parameters for your data and your problem.
  7. Deploy your model: It is important to deploy your model in a machine learning algorithm in order to make predictions or classifications. The algorithms will be able to use the model to more accurately predict outcomes or classify objects. Additionally, the deployment of the model will help improve performance and optimize the results of the machine learning process.
  8. Retrain your model: As your data changes over time, you’ll need to retrain your model to keep it accurate. There are a few different ways to retrain your model. One way is to simply start from scratch with a new training set. This can be time-consuming, but it gives you the opportunity to completely revamp your model if needed. Another way is to incrementally update your existing model using only the new data points. This is often more efficient, but it can lead to suboptimal results if not done correctly.

Model Optimization

Machine learning optimization is important for a number of reasons. First, it can help improve the accuracy of your models. Second, it can help you reduce the amount of training data needed to train your models. Third, it can help you enable faster and more efficient training of your models. Finally, machine learning optimization can help you avoid overfitting your models to the training data.

Machine learning optimization is a process that helps you select the best possible settings for your machine learning algorithms so that they will perform well on new data. The process involves finding the combination of algorithm settings that results in the highest accuracy on a validation set or test set.

There are a few different types of optimization techniques you can use for machine learning models: grid search, random search, and Bayesian search.

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Source: serokell.io

1. Exhaustive search, also known as brute-force searching, is the act of examining each potential hyperparameter to see whether it is a suitable match. When you forget the code for your bike’s lock and try out all of the possible options, you’re doing something similar in machine learning. The basic approach is straightforward. If you’re using a k-means algorithm, for example, you’ll have to search for the suitable number of clusters manually. However, if there are hundreds or thousands of alternatives to consider, it becomes too time consuming and heavy. In most real-world scenarios, brute-force search is ineffective.

2. Gradient descent is the most common approach for model advancement in order to reduce error. You must iterate over the training data and re-train the model at each iteration to implement gradient descent. Because it shows that you can achieve the lowest possible error while also improving the model’s accuracy, you want to minimize the cost function.

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Source: serokell.io

3. Generic Algorithms is an idea to apply evolution theory to machine learning. Only those organisms that have the greatest adaptation mechanisms survive and reproduce in the evolution theory. In machine learning, how do you determine which specimens are and aren’t the best?

Consider you’ve got a collection of unstructured algorithms. This will be your population. Some models are superior suited than others, and there are a variety of different models with some predetermined hyperparameters. Let’s see how we do it! To begin, you evaluate the accuracy of each model first. Then, only those that performed best are kept and used to generate new models by combining their parameters randomly. The new models are evaluated and the cycle repeats until we have a model that generalizes well.

Genetic algorithms are interesting because they can optimize a solution without being given any information about the problem other than what is necessary to evaluate candidate solutions. This is different from most optimization techniques, which require derivatives or some other form of problem-specific information.

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Source: serokell.io

Conclusion

Deep learning and machine learning require a high level of subject matter knowledge, access to richly labeled data, as well as computational resources for model training and improvement.

Improving machine learning models requires an art that may be learned by systematically correcting the faults of the current model. In this post, I’ve outlined a variety of techniques for improving and updating models to achieve desired performance levels while minimizing data usage.

14 Essential Machine Learning Algorithms

A subset of artificial intelligence, machine learning is a class of methods for automatically creating models from data. Using the relationships derived from the training dataset, these models are then able to make predictions on unseen data. Machine learning algorithms are the engine for machine learning because they turn a dataset into a model. Different types of algorithms learn differently (supervised learning, unsupervised learning, reinforcement learning) and perform different functions (classification, regression, natural language processing, and so on).

The algorithm you select depends on the type of machine learning problem you’re solving, available computing resources, and the nature of the dataset (eg: labeled vs. unlabeled). Generally, machine learning algorithms are used for classification or prediction problems. When a model is “fit” on a dataset, it learns from the data by recognizing patterns in the data.

Remember that machine learning algorithms are different machine learning models, although these terms are often used interchangeably. A model in machine learning is the output of a machine learning algorithm that has been fit on a dataset. The model represents what the algorithm has learned from the data—the rules, numbers, and other algorithm-specific data structures required to make predictions.

ML algorithms can be described using math and pseudocode (a representation of code that can be understood by a layman). Algorithmic pseudocode is a plain language description of the steps in an algorithm. 

In the real world, machine learning algorithms are used on massive datasets to perform a range of prediction tasks, such as powering recommendation engines and performing spam and fraud detection, risk assessments, image and text classification, natural language processing, sentiment analysis, and so much more. 

Is machine learning the right career for you? Find out if you’re eligible for Springboard’s Machine Learning Career Track.

What are the 3 Types of Machine Learning Algorithms?

Machine learning algorithms can be programmed to learn from data in different ways. The most common types of machine learning algorithms make use of supervised learning, unsupervised learning, and reinforcement learning.

1. Supervised learning

Supervised learning algorithms learn by example. The programmer provides the machine learning algorithm with a known dataset that includes desired inputs and outputs (such as input images and their corresponding labels), and the algorithm determines how to arrive at the desired output (known as “ground truth”) by identifying patterns in the training data.

The algorithm learns from these observations, makes predictions on test data, and is corrected by the programmer. In the end, the programmer picks the model or function that best describes the training data and makes the best estimation of output. Supervised learning is useful for image classification, regression, and forecasting.

2. Unsupervised learning

Unsupervised learning algorithms make predictions from untagged data, where there is no ground truth or known output. Unsupervised learning algorithms can discover hidden patterns or data groupings to analyze and cluster unlabeled datasets—making them the ideal solution for exploratory data analysis. The algorithm classifies, labels, and/or groups the data points without any human intervention.

While unsupervised learning can perform more complex data mining tasks than supervised learning algorithms, they can be more unpredictable, potentially adding categories and labels based on its interpretation of the training data. This type of algorithm whose logic can’t be explained in plain language is known as a “black box.” Unsupervised learning is useful for customer segmentation, anomaly detection in network traffic, and content recommendations. 

3. Reinforcement learning

Reinforcement learning algorithms follow a regimented learning process of trial-and-error. The algorithm is provided with a set of actions, parameters, and values—similar to the types of constraints players face in a game. The algorithm then tries to explore different options and possibilities within these predefined rules—a strategy for “winning” the game, if you will—while monitoring and evaluating the results to come up with the best solution to the problem.

To program the algorithm to do what you want, the AI gets rewards or punishments for actions it performs as signals for positive and negative behavior via an action-reward feedback loop. The algorithm’s goal is to find a suitable action model that will maximize the total reward. The algorithm learns from past mistakes (punishments) and adapts its approach to the situation to achieve the best possible result. Reinforcement learning is used in autonomous vehicles, recommendation engines, game development, robotics, and more. 

14 Machine Learning Algorithms—And How They Work

Here are the most common types of supervised, unsupervised, and reinforcement learning algorithms.

1. Linear Regression

Linear regression algorithms are a type of supervised learning algorithm that performs a regression task and are one of the most popular and well understood algorithms in the field of data science. Regression analysis is a type of predictive modeling that discovers the relationship between an input and the target variable. The most basic type of regression is linear regression, which shows the strength of the correlation between two variables, and whether that correlation is positive or negative.

The hypothesis function for regression equations is: hθ = θ + θ1x

In statistics, regression predicts a dependent variable (y) based on a given independent variable (x). The type of regression technique used depends on the number of independent variables and the type of relationship between the dependent and independent variables. In linear regression, there is only one independent variable and a linear relationship between the independent (x-axis) and dependent (y-axis) variable. Based on the given data points, the model attempts to plot a line that best describes the relationship between two variables. Regression algorithms predict the output values based on input features from data fed into the system.

What is it used for?

Today, regression models are used in financial forecasting, trend analysis, and time-series predictions.

2. Logistic Regression 

Logistic regression algorithms are the go-to for binary classification problems. There are two types of logistic regression: binary (eg: Does an input belong to the default class? Yes/No) and multilinear (Does the input image contain a dog? A cat? A sheep?). The algorithm maps predicted values to probabilities using the Sigmoid function, an S-shaped curve also known as the logistic function. The function maps any real value onto another value between 0 and 1, which expresses the probability that an input belongs to the default class. Inputs are combined linearly using weights or coefficient values to predict an output value (y). The best coefficients result in a model that predicts a value very close to one for the default class and very close to zero for the other class.

For example, if we are modeling people’s sex as male or female from their height, then the first class could be male and the logistic regression model could be written as the probability of male given a person’s height, or: 

P(sex=male|height) or P(X)=P(Y=1|X)

The probability prediction must be transformed into a binary value (0 or 1) in order to make a probability prediction.

What is it used for?

Logistic regression models are used for spam detection, fraud detection, and tumor image classification

3. Decision Tree

Decision trees are a type of supervised machine learning algorithm used for classification and regression problems in machine learning. With a flowchart-like structure, decision trees represent a sequential list of questions or features contained within a node. Each node branches into a subsequent node, with a final leaf node representing a class label (a decision taken after computing all features). In a decision tree, different features have different importance (represented by weights or percentages), and the relationships between them can be viewed easily. The paths from root to leaf represent classification rules. Decision trees come in two types: classification trees (yes/no types) or regression trees (continuous data types, such as a numerical value).

In decision analysis, a decision tree can be used to visually represent decisions and decision-making. When it comes to structured or tabular data, decision trees are considered the best method for model fitting. 

What is it used for?

Decision tree algorithms are used for evaluating loan applications, risk assessments, fraud detection, and medical diagnosis. 

4. Support Vector Machine (SVM)

Support Vector Machine is a supervised machine learning algorithm used for classification and regression problems. The purpose of SVM is to find a hyperplane in an N-dimensional space (where N equals the number of features) that classifies the input data into distinct groups. The hyperplane represents a decision boundary, whereby the data points that fall on either side of it belong to a distinct class based on their shared similarities. Consequently, the objective of an SVM is to find a hyperplane that has the maximum margin (the maximum distance between data points of either class), in order to draw this distinction. SVM takes the data as an input, transforms it using a technique called the kernel tricks, and finds an optimal boundary (hyperplane) between the data points.

The dimension of the hyperplane depends on the number of features. If the number of input features is two, the hyperplane is just a line. However, if the number of features is three, the hyperplane becomes a two-dimensional plane. Support vectors are the data points closest to the hyperplane and influence the position and orientation of the hyperplane. We compute the distance (margin) between the hyperplane and the support vectors. Again, the goal is to maximize the margin. The hyperplane with the maximum margin between the data points of either class is the optimal hyperplane. SVM is considered a black box as the decisions and complex data transformations are very difficult to interpret.

What is it used for?

SVM is used for facial recognition, handwriting recognition, image classification, text categorization, and more.

5. Naive Bayes

Naive Bayes is a probabilistic classification algorithm based on the Bayes Theorem in statistics and probability theory. While this algorithm is used for a variety of classification tasks, it works especially well for natural language processing. The Bayes Theorem is a simple mathematical formula for calculating conditional probabilities. Conditional probability is the measure of the probability of an event given that another event has occurred. Essentially, the formula tells you how often A happens given B, expressed as P(A|B), or vice versa.

The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome. For example, the condition of a fruit being red, round, and 2-4 inches in diameter are features that must all be present for it to be classified as an apple, but the algorithm perceives each feature as distinct. If given a sentence—for example, “the sky is blue”—the algorithm will interpret the individual words and not the entire sentence, even though words that stand next to each other in a sentence influence the meaning of the sentence. Hence why the algorithm is referred to as “naive.” Even though the independence assumption is generally incorrect in real-world situations, the Naive Bayes classifier is useful for large datasets because it works extremely fast relative to other classification algorithms, and has been known to outperform even highly sophisticated classification methods.

What is it used for?

Naive Bayes is used for image classification, document classification (eg: classifying texts into different languages, genres, or topics through the presence of keywords), and sentiment analysis (calculating the probability of a sentence or paragraph being positive or negative).

6. K-Nearest Neighbors (KNN)

K-nearest neighbors is a supervised machine learning algorithm used to solve classification and regression problems. The algorithm assumes that similar data points exist in close proximity. KNN captures the idea of similarity by calculating the straight-line distance (AKA the Euclidean distance) between points on a graph. The algorithm then tries to establish nonlinear boundaries for each class, similar to an SVM. In KNN classification, an object is assigned to the class most common amongst its ‘k’ nearest neighbors, where k is a user-defined constant. Increasing k (up to a point) results in more stable predictions due to majority voting/averaging, and the algorithm is thus more likely to make accurate predictions. When k=1, the object is simply assigned to the class of its single nearest neighbor. Increasing k is akin to casting a wider fishing net, where the average is computed from a larger, more representative sample of “fish.” In KNN regression, the output is the property value for the object—meaning the value of all the average values of the K-nearest neighbors.

It is useful to assign weights to the contributions of the neighbors so that the nearest neighbors contribute more to the average than distant ones. This also prevents bias when the class distribution is skewed. Samples of a more prevalent class tend to dominate the prediction of a new example because they tend to be common among the k-nearest neighbors due to their large number.

KNN works by finding the distances between a query and all the examples in the data, selecting the specified number of examples (k) closest to the query, then voting for the most frequent label (in the case of classification) or averaging the labels (in the case of regression).

What is it used for?

KNN is used in recommender systems, such as products on Amazon, movies on Netflix, and videos on YouTube.

7. K-Means

K-means is a method of vector quantization that aims to separate n observations into k clusters. Unlike KNN, K-Means is used to find groups that have not been explicitly labeled in the data. Clustering is one of the most common exploratory data analysis techniques because it can be used to find out what groups exist or to identify unknown groups from complex datasets. Clustering is the process of identifying non-overlapping subgroups (clusters) within the data such that the data points in the same cluster are very similar, while the data points in other clusters are very different (i.e. far apart according to Euclidean distance or correlation-based distance). The objective of K-means is to group similar data points together and discover underlying patterns. 

The target number, k, refers to the number of centroids you need in the dataset. A centroid represents the center of the cluster—the arithmetic mean of every data point that belongs to that cluster. The algorithm attempts to minimize the sum of the squared distance between data points and the centroid.

Clustering can be done based on subgroups or features. Since clustering algorithms use distance-based measurements to determine the similarity between data points, it’s best to standardize the data to have a mean of zero and a standard deviation of one. In most datasets, the features have different units of measurement (eg: height vs. weight), so these units must be standardized.

What is it used for?

K-means is used for market segmentation when we try to find customers that are similar to each in terms of behavior/attributes, image recognition, and document clustering. 

8. Random Forest 

Random forest is a supervised learning algorithm used for classification, regression, and other tasks. The algorithm consists of a multitude of decision trees—known as a “forest”—which have been trained with the bagging method. The general idea of the bagging method is that a combination of learning models increases the accuracy of the overall result. This method is known as ensemble learning, a technique that combines many classifiers to provide solutions to complex problems. Random forest builds multiple decision trees and merges their outputs (predictions) by taking the mean or average of all the outputs to obtain a more stable and accurate prediction. 

In every random forest, a subset of features is selected randomly at each node’s splitting point, where the randomized feature selection reduces bias. Consequently, random forest overcomes the limitations of the decision tree algorithm by reducing overfitting of datasets and increasing precision.

9. Dimensionality reduction 

Dimensionality reduction algorithms are used for feature selection and feature extraction. In machine learning classification problems, there are often too many variables that form the basis of a classification. These variables are called features. The higher the number of features, the harder it is to make predictions from the training set. Oftentimes, most of these features are correlated, hence redundant. For example, if 95% of observations were for 35-year-old women, then age and gender variables can be eliminated without losing too much information. Some features have nothing to do with the target variable; others might be correlated to the target variable but have no causal relationship to it.

If redundancies aren’t eliminated, models will try to map any feature included in the dataset to the target variable even if there is no relationship between them, which leads to imprecision.

Dimensionality reduction is the process of reducing the number of random variables under consideration and instead of obtaining a set of principal variables. In machine learning, dimensionality refers to the number of features in your dataset. When there’s an insufficient number of observations for each feature, the algorithm may struggle to train models effectively because the model doesn’t have enough samples for each feature. This is known as the “curse of dimensionality” and is especially relevant for clustering algorithms that rely on distance calculations. 

Feature selection refers to filtering irrelevant or redundant features from your dataset, while feature extraction involves compressing near-identical features into a lower-dimensional space.

What is it used for?

Dimensionality reduction is a necessary procedure when working with large datasets because it results in data compression, hence reduced storage space and computing power.

10. Gradient boosting algorithms 

Gradient boosting algorithms are another example of ensemble learning, where weak prediction models are combined to create a more powerful new model. Boosting is a method for creating an ensemble. It starts by fitting an initial model, such as a tree or linear regression, to the data. Then a second model is created to predict the cases where the first model performs poorly. This process is repeated many times, where each subsequent model attempts to correct the shortcomings of the combined boosted ensemble of all previous models.

A weak model refers to one whose performance is slightly better than random chance. Gradient boosting is one of the most powerful algorithms in the field of machine learning. It refers to a family of machine learning algorithms that convert weak learners into stronger models in an iterative process.

The term ‘gradient boosting’ refers to the use of a gradient descent algorithm to minimize the loss when adding new models. Target outcomes for each case are set based on the gradient of the error with respect to the prediction. XGBoost

11. XGBoost

XGBoost is a decision tree algorithm that uses a gradient boosting framework. Developed as a research project at the University of Washington, XGBoost is the most popular gradient boosting R package and has been widely used in cutting-edge industry applications and Kaggle competitions. XGBoost, which stands for “Extreme Gradient Boosting,” is an optimized distributed gradient boosting library designed to be more efficient, flexible, and portable than gradient boosted decision trees. Features of XGBoost include regularized learning, which helps to smooth the final learned weights to avoid model overfitting. Also, the tree ensemble cannot be optimized using traditional optimization methods such as Euclidean space (the distance measure that determines accuracy in classification models), so the model is trained in an additive manner. 

As an open-source implementation tool, XGBoost belongs to a broader collection of tools under the Distributed (Deep) Machine Learning Community on GitHub.

What is it used for?

According to the researchers who came up with it, the most important factor about the XGBoost is its scalability and speed, with a system that runs more than 10x faster than existing popular solutions on a single machine while enabling data scientists to process hundreds of millions of examples on a standard desktop computer.

12. GBM (Gradient Boosting Machine)

Gradient Boosting Machine is the original gradient boosting framework for decision trees introduced in 2001 by Jerome H. Friedman, a professor of statistics at Stanford University. Also known as MART (Multiple Additive Regression Trees) and GBRT (Gradient Boosted Regression Trees). GBM identifies weak learners by using gradients in the loss function (y=ax+b+e, where e is the error term). The loss function measures how good a model’s coefficients are at fitting the underlying data. Put more simply, the loss function indicates the difference between true values and predicted values.

13. LightGBM

Another free and open-source gradient boosting framework for decision tree algorithms, LightGBM was initially developed by Microsoft. LightGBM has many of the same advantages as XGBoost, including sparse organization, parallel training (training different layers of a model on different GPUs to reduce training time), multiple loss functions, regularization (a technique used for tuning the function) , and early stopping.

The main difference between XGBoost and LightGBM lies in the construction of the decision trees. LightGBM does not grow a tree row by row as most other implementations do. Instead, it chooses the leaf (terminal node) it believes will yield the largest decrease in loss (which is the main objective of gradient boosting). Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks such as XGBoost on both efficiency and accuracy, with lower memory consumption.

14. CatBoost 

Yet another open-source gradient boosting library for decision trees, CatBoost was developed by researchers and engineers at Yandex, a Russian-Dutch internet company. The library is used for search, recommendation systems, personal assistants, self-driving cars, weather prediction, and many other tasks at Yandex and other companies.

Cloudflare, a web security company, used the CatBoost algorithm to build a machine learning model that would counteract credential stuffing bots—cyberattacks that attempt to log into and take over a user’s account by assaulting password forms with previously stolen credentials. CatBoost provides great results with default parameters, so there is no need to spend too much time on parameter tuning. You can also use non-numeric factors instead of having to pre-process your data and turn it into numbers. 

What Are the Best Machine Learning Algorithms?

Machine learning enables businesses to analyze massive datasets to gain insights about their customers, make forecasts about future events, and provide a personalized customer experience. Fundamentally, machine learning extracts meaningful insights from raw data to solve complex business problems. However, no one machine learning algorithm works best for every problem—hence the concept of the “no free lunch” theorem in supervised machine learning. Predictive analytics is the most common type of machine learning, which involves the mapping Y=f(x) to make predictions of Y for new X. Linear regression is widely used for predicting sales revenue, setting prices, and analyzing risk in the financial and insurance sectors.

The “best” algorithms to use varies widely by industry, depending on the nature of the business problem and the type of data available. For example, image recognition is becoming increasingly prevalent in healthcare for its usefulness in medical diagnostics, such as classifying an MRI of a brain tumor as benign or malignant—an example of a binary classification problem that can be solved using logistic regression. Meanwhile, retailers use a range of machine learning algorithms from simple linear regression to Naive Bayes to improve inventory control using predictive analytics, perform sentiment analysis to predict the likelihood of customer churn, and using K-means clustering for customer segmentation.

Meanwhile, the financial and insurance industries use decision trees and corresponding gradient boosting libraries such as XGBoost, CatBoost, and GBM to evaluate loan and mortgage applications, detect fraud, and perform risk assessments before entering new markets. While some algorithms are used more than others in certain industries, every type of algorithm has a significant role to play in developing machine learning models that are more accurate and less prone to bias.

10 Best Machine Learning Algorithms

Though we’re living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in certain cases 70 years old.

Some might contend that many of these older methods fall into the camp of ‘statistical analysis’ rather than machine learning, and prefer to date the advent of the sector back only so far as 1957, with the invention of the Perceptron.

Given the extent to which these older algorithms support and are enmeshed in the latest trends and headline-grabbing developments in machine learning, it’s a contestable stance. So let’s take a look at some of the ‘classic’ building blocks underpinning the latest innovations, as well as some newer entries that are making an early bid for the AI hall of fame.

1: Transformers

In 2017 Google Research led a research collaboration culminating in the paper Attention Is All You Need. The work outlined a novel architecture that promoted attention mechanisms from ‘piping’ in encoder/decoder and recurrent network models to a central transformational technology in their own right.

The approach was dubbed Transformer, and has since become a revolutionary methodology in Natural Language Processing (NLP), powering, amongst many other examples, the autoregressive language model and AI poster-child GPT-3.

Transformers elegantly solved the problem of sequence transduction, also called ‘transformation’, which is occupied with the processing of input sequences into output sequences. A transformer also receives and manages data in a continuous manner, rather than in sequential batches, allowing a ‘persistence of memory’ which RNN architectures are not designed to obtain. For a more detailed overview of transformers, take a look at our reference article.

In contrast to the Recurrent Neural Networks (RNNs) that had begun to dominate ML research in the CUDA era, Transformer architecture could also be easily parallelized, opening the way to productively address a far larger corpus of data than RNNs.

Popular Usage

Transformers captured the public imagination in 2020 with the release of OpenAI’s GPT-3, which boasted a then record-breaking 175 billion parameters. This apparently staggering achievement was eventually overshadowed by later projects, such as the 2021 release of Microsoft’s Megatron-Turing NLG 530B, which (as the name suggests) features over 530 billion parameters.

A timeline of hyperscale Transformer NLP projects. Source: Microsoft

A timeline of hyperscale Transformer NLP projects. Source: Microsoft

Transformer architecture has also crossed over from NLP to computer vision, powering a new generation of image synthesis frameworks such as OpenAI’s CLIP and DALL-E, which use text>image domain mapping to finish incomplete images and synthesize novel images from trained domains, among a growing number of related applications.

DALL-E attempts to complete a partial image of a bust of Plato. Source: https://openai.com/blog/dall-e/

DALL-E attempts to complete a partial image of a bust of Plato. Source: https://openai.com/blog/dall-e/

2: Generative Adversarial Networks (GANs)

Though transformers have gained extraordinary media coverage through the release and adoption of GPT-3, the Generative Adversarial Network (GAN) has become a recognizable brand in its own right, and may eventually join deepfake as a verb.

First proposed in 2014 and primarily used for image synthesis, a Generative Adversarial Network architecture is composed of a Generator and a Discriminator. The Generator cycles through thousands of images in a dataset, iteratively attempting to reconstruct them. For each attempt, the Discriminator grades the Generator’s work, and sends the Generator back to do better, but without any insight into the way that the previous reconstruction erred.

Source: https://developers.google.com/machine-learning/gan/gan_structure

Source: https://developers.google.com/machine-learning/gan/gan_structure

This forces the Generator to explore a multiplicity of avenues, instead of following the potential blind alleys that would have resulted if the Discriminator had told it where it was going wrong (see #8 below). By the time the training is over, the Generator has a detailed and comprehensive map of relationships between points in the dataset.

An excerpt from the researchers' accompanying video (see embed at end of article). Note that the user is manipulating the transformations with a 'grab' cursor (top left). Source: https://www.youtube.com/watch?v=k7sG4XY5rIc

From the paper Improving GAN Equilibrium by Raising Spatial Awareness: a novel framework cycles through the sometimes-mysterious latent space of a GAN, providing responsive instrumentality for an image synthesis architecture. Source: https://genforce.github.io/eqgan/

By analogy, this is the difference between learning a single humdrum commute to central London, or painstakingly acquiring The Knowledge.

The result is a high-level collection of features in the latent space of the trained model. The semantic indicator for a high level feature could be ‘person’, whilst a descent through specificity related to the feature may unearth other learned characteristics, such as ‘male’ and ‘female’. At lower levels the sub-features can break down to, ‘blonde’, ‘Caucasian’, et al.

Entanglement is a notable issue in the latent space of GANs and encoder/decoder frameworks: is the smile on a GAN-generated female face an entangled feature of her ‘identity’ in the latent space, or is it a parallel branch?

GAN-generated faces from thispersondoesnotexist. Source: https://this-person-does-not-exist.com/en

GAN-generated faces from thispersondoesnotexist. Source: https://this-person-does-not-exist.com/en

The past couple of years have brought forth a growing number of new research initiatives in this respect, perhaps paving the way for feature-level, Photoshop-style editing for the latent space of a GAN, but at the moment, many transformations are effectively ‘all or nothing’ packages. Notably, NVIDIA’s EditGAN release of late 2021 achieves a high level of interpretability in the latent space by using semantic segmentation masks.

Popular Usage

Beside their (actually fairly limited) involvement in popular deepfake videos, image/video-centric GANs have proliferated over the last four years, enthralling researchers and the public alike. Keeping up with the dizzying rate and frequency of new releases is a challenge, though the GitHub repository Awesome GAN Applications aims to provide a comprehensive list.

Generative Adversarial Networks can in theory derive features from any well-framed domain, including text.

3: SVM

Originated in 1963, Support Vector Machine (SVM) is a core algorithm that crops up frequently in new research. Under SVM, vectors map the relative disposition of data points in a dataset, while support vectors delineate the boundaries between different groups, features, or traits.

Support vectors define the boundaries between groups. Source: https://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.html

Support vectors define the boundaries between groups. Source: https://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.html

The derived boundary is called a hyperplane.

At low feature levels, the SVM is two-dimensional (image above), but where there’s a higher recognized number of groups or types, it becomes three-dimensional.

A deeper array of points and groups necessitates a three-dimensional SVM. Source: https://cml.rhul.ac.uk/svm.html

A deeper array of points and groups necessitates a three-dimensional SVM. Source: https://cml.rhul.ac.uk/svm.html

Popular Usage

Since support Vector Machines can effectively and agnostically address high-dimensional data of many kinds, they crop up widely across a variety of machine learning sectors, including deepfake detection, image classification, hate speech classification, DNA analysis and population structure prediction, among many others.

4: K-Means Clustering

Clustering in general is an unsupervised learning approach that seeks to categorize data points through density estimation, creating a map of the distribution of the data being studied.

K-Means clustering divines segments, groups and communities in data. Source: https://aws.amazon.com/blogs/machine-learning/k-means-clustering-with-amazon-sagemaker/

K-Means clustering divines segments, groups and communities in data. Source: https://aws.amazon.com/blogs/machine-learning/k-means-clustering-with-amazon-sagemaker/

K-Means Clustering has become the most popular implementation of this approach, shepherding data points into distinctive ‘K Groups’, which may indicate demographic sectors, online communities, or any other possible secret aggregation waiting to be discovered in raw statistical data.

Clusters form in K-Means analysis. Source: https://www.geeksforgeeks.org/ml-determine-the-optimal-value-of-k-in-k-means-clustering/

The K value itself is the determinant factor in the utility of the process, and in establishing an optimal value for a cluster. Initially, the K value is randomly assigned, and its features and vector characteristics compared to its neighbors. Those neighbors that most closely resemble the data point with the randomly assigned value get assigned to its cluster iteratively until the data has yielded all the groupings that the process permits.

The plot for the squared error, or ‘cost’ of differing values among the clusters will reveal an elbow point for the data:

The 'elbow point' in a cluster graph. Source: https://www.scikit-yb.org/en/latest/api/cluster/elbow.html

The elbow point is similar in concept to the way that loss flattens out to diminishing returns at the end of a training session for a dataset. It represents the point at which no further distinctions between groups is going to become apparent, indicating the moment to move on to subsequent phases in the data pipeline, or else to report findings.

Popular Usage

K-Means Clustering, for obvious reasons, is a primary technology in customer analysis, since it offers a clear and explainable methodology to translate large quantities of commercial records into demographic insights and ‘leads’.

Outside of this application, K-Means Clustering is also employed for landslide prediction, medical image segmentation, image synthesis with GANs, document classification, and city planning, among many other potential and actual uses.

5: Random Forest

Random Forest is an ensemble learning method that averages the result from an array of decision trees to establish an overall prediction for the outcome.

Source: https://www.tutorialandexample.com/wp-content/uploads/2019/10/Decision-Trees-Root-Node.png

If you’ve researched it even as little as watching the Back to the Future trilogy, a decision tree itself is fairly easy to conceptualize: a number of paths lie before you, and each path branches out to a new outcome which in turn contains further possible paths.

In reinforcement learning, you might retreat from a path and start again from an earlier stance, whereas decision trees commit to their journeys.

Thus the Random Forest algorithm is essentially spread-betting for decisions. The algorithm is called ‘random’ because it makes ad hoc selections and observations in order to understand the median sum of the results from the decision tree array.

Since it takes into account a multiplicity of factors, a Random Forest approach can be more difficult to convert into meaningful graphs than a decision tree, but is likely to be notably more productive.

Decision trees are subject to overfitting, where the results obtained are data-specific and not likely to generalize. Random Forest’s arbitrary selection of data points combats this tendency, drilling through to meaningful and useful representative trends in the data.

Decision tree regression. Source: https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html

Popular Usage

As with many of the algorithms in this list, Random Forest typically operates as an ‘early’ sorter and filter of data, and as such consistently crops up in new research papers. Some examples of Random Forest usage include Magnetic Resonance Image Synthesis, Bitcoin price prediction, census segmentation, text classification and credit card fraud detection.

Since Random Forest is a low-level algorithm in machine learning architectures, it can also contribute to the performance of other low-level methods, as well as visualization algorithms, including Inductive Clustering, Feature Transformations, classification of text documents using sparse features, and displaying Pipelines.

6: Naive Bayes

Coupled with density estimation (see 4, above), a naive Bayes classifier is a powerful but relatively lightweight algorithm capable of estimating probabilities based on the calculated features of data.

Feature relationships in a naive Bayes classifier. Source: https://www.sciencedirect.com/topics/computer-science/naive-bayes-model

The term ‘naïve’ refers to the assumption in Bayes’ theorem that features are unrelated, known as conditional independence. If you adopt this standpoint, walking and talking like a duck aren’t enough to establish that we’re dealing with a duck, and no ‘obvious’ assumptions are prematurely adopted.

This level of academic and investigative rigor would be overkill where ‘common sense’ is available, but is a valuable standard when traversing the many ambiguities and potentially unrelated correlations that may exist in a machine learning dataset.

In an original Bayesian network, features are subject to scoring functions, including minimal description length and Bayesian scoring, which can impose restrictions on the data in terms of the estimated connections found between the data points, and the direction in which these connections flow.

A naive Bayes classifier, conversely, operates by assuming that the features of a given object are independent, subsequently using Bayes’ theorem to calculate the probability of a given object, based on its features.

Popular Usage

Naive Bayes filters are well-represented in disease prediction and document categorization, spam filtering, sentiment classification, recommender systems, and fraud detection, among other applications.

7: K- Nearest Neighbors (KNN)

First proposed by the US Air Force School of Aviation Medicine in 1951, and having to accommodate itself to the state-of-the-art of mid-20th century computing hardware, K-Nearest Neighbors (KNN) is a lean algorithm that still features prominently across academic papers and private sector machine learning research initiatives.

KNN has been called ‘the lazy learner’, since it exhaustively scans a dataset in order to evaluate the relationships between data points, rather than requiring the training of a full-fledged machine learning model.

A KNN grouping. Source: https://scikit-learn.org/stable/modules/neighbors.html

Though KNN is architecturally slender, its systematic approach does place a notable demand on read/write operations, and its use in very large datasets can be problematic without adjunct technologies such as Principal Component Analysis (PCA), which can transform complex and high volume datasets into representative groupings that KNN can traverse with less effort.

A recent study evaluated the effectiveness and economy of a number of algorithms tasked to predict whether an employee will leave a company, finding that the septuagenarian KNN remained superior to more modern contenders in terms of accuracy and predictive effectiveness.

Popular Usage

For all its popular simplicity of concept and execution, KNN is not stuck in the 1950s – it’s been adapted into a more DNN-focused approach in a 2018 proposal by Pennsylvania State University, and remains a central early-stage process (or post-processing analytical tool) in many far more complex machine learning frameworks.

In various configurations, KNN has been used or for online signature verification, image classification, text mining, crop prediction, and facial recognition, besides other applications and incorporations.

A KNN-based facial recognition system in training. Source: https://pdfs.semanticscholar.org/6f3d/d4c5ffeb3ce74bf57342861686944490f513.pdf

8: Markov Decision Process (MDP)

A mathematical framework introduced by American mathematician Richard Bellman in 1957, The Markov Decision Process (MDP) is one of the most basic blocks of reinforcement learning architectures. A conceptual algorithm in its own right, it has been adapted into a great number of other algorithms, and recurs frequently in the current crop of AI/ML research.

MDP explores a data environment by using its evaluation of its current state (i.e. ‘where’ it is in the data) to decide which node of the data to explore next.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0888613X18304420

A basic Markov Decision Process will prioritize near-term advantage over more desirable long-term objectives. For this reason, it is usually embedded into the context of a more comprehensive policy architecture in reinforcement learning, and is often subject to limiting factors such as discounted reward, and other modifying environmental variables that will prevent it from rushing to an immediate goal without consideration of the broader desired outcome.

Popular Usage

MDP’s low-level concept is widespread in both research and active deployments of machine learning. It’s been proposed for IoT security defense systems, fish harvesting, and market forecasting.

Besides its obvious applicability to chess and other strictly sequential games, MDP is also a natural contender for the procedural training of robotics systems, as we can see in the video below.

Global Planner using a Markov Decision Process – Mobile Industrial Robotics

9: Term Frequency-Inverse Document Frequency

Term Frequency (TF) divides the number of times a word appears in a document by the total number of words in that document. Thus the word seal appearing once in a thousand-word article has a term frequency of 0.001. By itself, TF is largely useless as an indicator of term importance, due to the fact that meaningless articles (such as aandthe, and it) predominate.

To obtain a meaningful value for a term, Inverse Document Frequency (IDF) calculates the TF of a word across multiple documents in a dataset, assigning low rating to very high-frequency stopwords, such as articles. The resulting feature vectors are normalized to whole values, with each word assigned an appropriate weight.

TF-IDF weights the relevance of terms based on frequency across a number of documents, with rarer occurrence an indicator of salience. Source: https://moz.com/blog/inverse-document-frequency-and-the-importance-of-uniqueness

TF-IDF weights the relevance of terms based on frequency across a number of documents, with rarer occurrence an indicator of salience. Source: https://moz.com/blog/inverse-document-frequency-and-the-importance-of-uniqueness

Though this approach prevents semantically important words from being lost as outliers, inverting the frequency weight does not automatically mean that a low-frequency term is not an outlier, because some things are rare and worthless. Therefore a low-frequency term will need to prove its value in the wider architectural context by featuring (even at a low frequency per document) in a number of documents in the dataset.

Despite its age, TF-IDF is a powerful and popular method for initial filtering passes in Natural Language Processing frameworks.

Popular Usage

Because TF-IDF has played at least some part in the development of Google’s largely occult PageRank algorithm over the last twenty years, it has become very widely adopted as a manipulative SEO tactic, in spite of John Mueller’s 2019 disavowal of its importance to search results.

Due to the secrecy around PageRank, there is no clear evidence that TF-IDF is not currently an effective tactic for rising in Google’s rankings. Incendiary discussion among IT professionals lately indicates a popular understanding, correct or not, that term abuse may still result in improved SEO placement (though additional accusations of monopoly abuse and excessive advertising blur the confines of this theory).

10: Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is an increasingly popular method for optimizing the training of machine learning models.

Gradient Descent itself is a method of optimizing and subsequently quantifying the improvement that a model is making during training.

In this sense, ‘gradient’ indicates a slope downwards (rather than a color-based gradation, see image below), where the highest point of the ‘hill’, on the left, represents the beginning of the training process. At this stage the model has not yet seen the entirety of the data even once, and has not learned enough about relationships between the data to produce effective transformations.

A gradient descent on a FaceSwap training session. We can see that the training has plateaued for some time in the second half, but has eventually recovered its way down the gradient towards an acceptable convergence.

A gradient descent on a FaceSwap training session. We can see that the training has plateaued for some time in the second half, but has eventually recovered its way down the gradient towards an acceptable convergence.

The lowest point, on the right, represents convergence (the point at which the model is as effective as it is ever going to get under the imposed constraints and settings).

The gradient acts as a record and predictor for the disparity between the error rate (how accurately the model has currently mapped the data relationships) and the weights (the settings that influence the way in which the model will learn).

This record of progress can be used to inform a learning rate schedule, an automatic process that tells the architecture to become more granular and precise as the early vague details transform into clear relationships and mappings. In effect, gradient loss provides a just-in-time map of where the training should go next, and how it should proceed.

The innovation of Stochastic Gradient Descent is that it updates the model’s parameters on each training example per iteration, which generally speeds up the journey to convergence. Due to the advent of hyperscale datasets in recent years, SGD has grown in popularity lately as one possible method to address the ensuing logistic issues.

On the other hand, SGD has negative implications for feature scaling, and may require more iterations to achieve the same result, requiring additional planning and additional parameters, compared to regular Gradient Descent.

Popular Usage

Due to its configurability, and in spite of its shortcomings, SGD has become the most popular optimization algorithm for fitting neural networks. One configuration of SGD that is becoming dominant in new AI/ML research papers is the choice of the Adaptive Moment Estimation (ADAM, introduced in 2015) optimizer.

ADAM adapts the learning rate for each parameter dynamically (‘adaptive learning rate’), as well as incorporating results from previous updates into the subsequent configuration (‘momentum’). Additionally, it can be configured to use later innovations, such as Nesterov Momentum.

However, some maintain that the use of momentum can also speed ADAM (and similar algorithms) to a sub-optimal conclusion. As with most of the bleeding edge of the machine learning research sector, SGD is a work in progress.