Overview of Supervised, Unsupervised, and Reinforcement Learning
Requirements
Interest in machine learning
Description
Course Outcome:
Learners completing this course will be able to give definitions and explain the types of problems that can be solved by the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
Course Topics and Approach:
This course gives a gentle introduction to the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning. The goal is to explain the key ideas using examples with many plots and animations and little math, so that the material can be accessed by a wide range of learners. The lectures are supplemented by Python demos, which show machine learning in action. Learners are encouraged to experiment with the course demo codes. Additionally, information about machine learning resources is provided, including sources of data and publicly available software packages.
Course Audience:
This course has been designed for ALL LEARNERS!!!
Course does not go into detail into the underlying math, so no specific math background is required
No previous experience with machine learning is required
No previous experience with Python (or programming in general) is required to be able to experiment with the course demo codes
Teaching Style and Resources:
Course includes many examples with plots and animations used to help students get a better understanding of the material
All resources, including course codes, Powerpoint presentations, info on additional resources, can be downloaded from the course Github site
Python Demos:
There are several options for running the Python demos:
Run online using Google Colab (With this option, demo codes can be run completely online, so no downloads are required. A Google account is required.)
Run on local machine using the Anaconda platform (This is probably best approach for those who would like to run codes locally, but don’t have python on their local machine. Demo video shows where to get free community version of Anaconda platform and how to run the codes.)
Run on local machine using python (This approach may be most suitable for those who already have python on their machines)
2021.09.28 Update
Section 5: update course codes, Powerpoint presentations, and videos so that codes are compatible with more recent versions of the Anaconda platform and plotting package
Who this course is for:
People curious about machine learning and data science
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.
SKILLS YOU WILL GAIN
Computational Neuroscience
Artificial Neural Network
Reinforcement Learning
Biological Neuron Model
Syllabus – What you will learn from this course
Content Rating95%(8,784 ratings)
WEEK1
4 hours to complete
Introduction & Basic Neurobiology (Rajesh Rao)
This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.
6 videos (Total 89 min), 6 readings, 2 quizzesSee All
WEEK2
4 hours to complete
What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
This module introduces you to the captivating world of neural information coding. You will learn about the technologies that are used to record brain activity. We will then develop some mathematical formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we investigate variability and noise in the brain, and how our models can accommodate them.
8 videos (Total 167 min), 3 readings, 1 quizSee All
WEEK3
3 hours to complete
Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
In this module, we turn the question of neural encoding around and ask: can we estimate what the brain is seeing, intending, or experiencing just from its neural activity? This is the problem of neural decoding and it is playing an increasingly important role in applications such as neuroprosthetics and brain-computer interfaces, where the interface must decode a person’s movement intentions from neural activity. As a bonus for this module, you get to enjoy a guest lecture by well-known computational neuroscientist Fred Rieke.
6 videos (Total 114 min), 2 readings, 1 quizSee All
WEEK4
3 hours to complete
Information Theory & Neural Coding (Adrienne Fairhall)
This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called our brain.
5 videos (Total 98 min), 2 readings, 1 quizSee All
WEEK5
4 hours to complete
Computing in Carbon (Adrienne Fairhall)
This module takes you into the world of biophysics of neurons, where you will meet one of the most famous mathematical models in neuroscience, the Hodgkin-Huxley model of action potential (spike) generation. We will also delve into other models of neurons and learn how to model a neuron’s structure, including those intricate branches called dendrites.7 videos (Total 114 min), 2 readings, 1 quizSee All
WEEK6
3 hours to complete
Computing with Networks (Rajesh Rao)
This module explores how models of neurons can be connected to create network models. The first lecture shows you how to model those remarkable connections between neurons called synapses. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. In the second lecture, you will learn about firing rate models and feedforward networks, which transform their inputs to outputs in a single “feedforward” pass. The last lecture takes you to the dynamic world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations, and more!SHOW ALL SYLLABUSSHOW ALL3 videos (Total 72 min), 2 readings, 1 quizSee All
WEEK7
3 hours to complete
Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)
This module investigates models of synaptic plasticity and learning in the brain, including a Canadian psychologist’s prescient prescription for how neurons ought to learn (Hebbian learning) and the revelation that brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures explore unsupervised learning and theories of brain function based on sparse coding and predictive coding.4 videos (Total 86 min), 2 readings, 1 quizSee All
WEEK8
3 hours to complete
Learning from Supervision and Rewards (Rajesh Rao)
In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov’s dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia. The grand finale: flying a helicopter using reinforcement learning!
SARSAAn algorithm for learning a Markov decision process policy
Q-learningA model-free reinforcement learning algorithm to learn the value of an action in a particular state.
Function approximation
Function approximatorsThe problem asks us to select a function among a well-defined class that closely matches (“approximates”) a target function in a task-specific way.
Policy-gradientValue-based, Policy-based, Actor-critic, policy-gradient, and softmax policy
REINFORCEA policy-gradient algorithm.
Deep reinforcement-learning
Deep Q-Network (DQN)A deep reinforcement-learning algorithm using experience reply and fixed Q-targets.
Deep Recurrent Q-Learning (DRQN)Deep reinforcement-learning algorithm for POMDP extends DQN and uses LSTM.
Optimistic Exploration with Pessimistic Initialization (OPIQ)A deep reinforcement-learning for MDP based on DQN.
Value Decomposition Networks (VDN)A multi-agent deep reinforcement-learning algorithm for Dec-POMDP.
QMIXA multi-agent deep reinforcement-learning algorithm for Dec-POMDP.
QTRANA multi-agent deep reinforcement-learning algorithm for Dec-POMDP.
Weighted QMIXA deep multi-agent reinforcement-learning for Dec-POMDP.
Resources
Wikipedia
David Silver’s Reinforcement-learning course
Who this course is for:
Anyone who interests in Deep reinforcement-learning
Configure and use the Unity Machine Learning Agents toolkit to solve physical problems in simulated environments
Understand the concepts of neural networks, supervised and deep reinforcement learning (PPO)
Apply ML control techniques to teach a go-kart to drive around a track in Unity
Requirements
Basic algebra and basic programming skills
Description
WARNING: take this class as a gentle introduction to machine learning, with particular focus on machine vision and reinforcement learning. The Unity project provided in this course is now obsolete because the Unity ML agents library is still in its beta version and the interface keeps changing all the time! Some of the implementation details you will find in this course will look different if you are using the latest release, but the key concepts and the background theory are still valid. Please refer to the official migrating documentation on the ml-agents github for the latest updates.
Learn how to combine the beauty of Unity with the power of Tensorflow to solve physical problems in a simulated environment with state-of-the-art machine learning techniques.
We study the problem of a go-kart racing around a simple track and try three different approaches to control it: a simple PID controller; a neural network trained via imitation (supervised) learning; and a neural network trained via deep reinforcement learning.
Each technique has its strengths and weaknesses, which we first show in a theoretical way at simple conceptual level, and then apply in a practical way. In all three cases the go-kart will be able to complete a lap without crashing.
We provide the Unity template and the files for all three solutions. Then see if you can build on it and improve performance further more.
Buckle up and have fun!
Who this course is for:
Students interested in a quick jump into machine learning, focusing on the application rather than the theory
Engineers looking for a machine learning realistic simulator
Semi-supervised learning (mixture of “labeled” and “unlabeled” data).
Reinforcement learning. Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process.
There are lots of overlaps in which ML algorithms are applied to a particular problem. As a result, for the same problem, there could be many different ML models possible. So, coming out with the best ML model is an art that requires a lot of patience and trial and error. Following figure provides a brief of all these learning types with sample use cases.
The supervised learning algorithms are a subset of the family of machine learning algorithms which are mainly used in predictive modeling. A predictive model is basically a model constructed from a machine learning algorithm and features or attributes from training data such that we can predict a value using the other values obtained from the input data. Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets. The main types of supervised learning algorithms include:
Classification algorithms: These algorithms build predictive models from training data which have features and class labels. These predictive models in-turn use the features learnt from training data on new, previously unseen data to predict their class labels. The output classes are discrete. Types of classification algorithms include decision trees, random forests, support vector machines, and many more.
Regression algorithms: These algorithms are used to predict output values based on some input features obtained from the data. To do this, the algorithm builds a model based on features and output values of the training data and this model is used to predict values for new data. The output values in this case are continuous and not discrete. Types of regression algorithms include linear regression, multivariate regression, regression trees, and lasso regression, among many others.
Some application of supervised learning are speech recognition, credit scoring, medical imaging, and search engines.
The unsupervised learning algorithms are the family of machine learning algorithms which are mainly used in pattern detection and descriptive modeling. However, there are no output categories or labels here based on which the algorithm can try to model relationships. These algorithms try to use techniques on the input data to mine for rules, detect patterns, and summarize and group the data points which help in deriving meaningful insights and describe the data better to the users. The main types of unsupervised learning algorithms include:
Clustering algorithms: The main objective of these algorithms is to cluster or group input data points into different classes or categories using just the features derived from the input data alone and no other external information. Unlike classification, the output labels are not known beforehand in clustering. There are different approaches to build clustering models, such as by using means, medoids, hierarchies, and many more. Some popular clustering algorithms include k-means, k-medoids, and hierarchical clustering.
Association rule learning algorithms: These algorithms are used to mine and extract rules and patterns from data sets. These rules explain relationships between different variables and attributes, and also depict frequent item sets and patterns which occur in the data. These rules in turn help discover useful insights for any business or organization from their huge data repositories. Popular algorithms include Apriori and FP Growth.
Some applications of unsupervised learning are customer segmentation in marketing, social network analysis, image segmentation, climatology, and many more.
Semi-Supervised Learning. In the previous two types, either there are no labels for all the observation in the dataset or labels are present for all the observations. Semi-supervised learning falls in between these two. In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters.
The reinforcement learning method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states.
In order to produce intelligent programs (also called agents), reinforcement learning goes through the following steps:
Input state is observed by the agent.
Decision making function is used to make the agent perform an action.
After the action is performed, the agent receives reward or reinforcement from the environment.
The state-action pair information about the reward is stored.
Some applications of the reinforcement learning algorithms are computer played board games (Chess, Go), robotic hands, and self-driving cars.
Predictive model
A predictive model is used for tasks that involve the prediction of one value using other values in the dataset. The learning algorithm attempts to discover and model the relationship between the target feature (the feature being predicted) and the other features. Despite the common use of the word “prediction” to imply forecasting, predictive models need not necessarily foresee events in the future. For instance, a predictive model could be used to predict past events, such as the date of a baby’s conception using the mother’s present-day hormone levels. Predictive models can also be used in real time to control traffic lights during rush hours.
Because predictive models are given clear instruction on what they need to learn and how they are intended to learn it, the process of training a predictive model is known as supervised learning. The supervision does not refer to human involvement, but rather to the fact that the target values provide a way for the learner to know how well it has learned the desired task. Stated more formally, given a set of data, a supervised learning algorithm attempts to optimize a function (the model) to find the combination of feature values that result in the target output.
So, supervised learning consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
The often used supervised machine learning task of predicting which category an example belongs to is known as classification. It is easy to think of potential uses for a classifier. For instance, you could predict whether:
An e-mail message is spam
A person has cancer
A football team will win or lose
An applicant will default on a loan
In classification, the target feature to be predicted is a categorical feature known as the class, and is divided into categories called levels. A class can have two or more levels, and the levels may or may not be ordinal. Because classification is so widely used in machine learning, there are many types of classification algorithms, with strengths and weaknesses suited for different types of input data.
Supervised learners can also be used to predict numeric data such as income, laboratory values, test scores, or counts of items. To predict such numeric values, a common form of numeric prediction fits linear regression models to the input data. Although regression models are not the only type of numeric models, they are, by far, the most widely used. Regression methods are widely used for forecasting, as they quantify in exact terms the association between inputs and the target, including both, the magnitude and uncertainty of the relationship.
Descriptive model
A descriptive model is used for tasks that would benefit from the insight gained from summarizing data in new and interesting ways. As opposed to predictive models that predict a target of interest, in a descriptive model, no single feature is more important than any other. In fact, because there is no target to learn, the process of training a descriptive model is called unsupervised learning. Although it can be more difficult to think of applications for descriptive models, what good is a learner that isn’t learning anything in particular – they are used quite regularly for data mining.
So, in unsupervised learning algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.
For example, the descriptive modeling task called pattern discovery is used to identify useful associations within data. Pattern discovery is often used for market basket analysis on retailers’ transactional purchase data. Here, the goal is to identify items that are frequently purchased together, such that the learned information can be used to refine marketing tactics. For instance, if a retailer learns that swimming trunks are commonly purchased at the same time as sunglasses, the retailer might reposition the items more closely in the store or run a promotion to “up-sell” customers on associated items.
The descriptive modeling task of dividing a dataset into homogeneous groups is called clustering. This is sometimes used for segmentation analysis that identifies groups of individuals with similar behavior or demographic information, so that advertising campaigns could be tailored for particular audiences. Although the machine is capable of identifying the clusters, human intervention is required to interpret them. For example, given five different clusters of shoppers at a grocery store, the marketing team will need to understand the differences among the groups in order to create a promotion that best suits each group.
Lastly, a class of machine learning algorithms known as meta-learners is not tied to a specific learning task, but is rather focused on learning how to learn more effectively. A meta-learning algorithm uses the result of some learnings to inform additional learning. This can be beneficial for very challenging problems or when a predictive algorithm’s performance needs to be as accurate as possible.
The following table lists only a fraction of the entire set of machine learning algorithms.
Model
Learning task
Supervised Learning Algorithms
Nearest Neighbor
Classification
Naive Bayes
Classification
Decision Trees
Classification
Classification Rule Learners
Classification
Linear Regression
Numeric prediction
Model Trees
Numeric prediction
Regression Trees
Neural Networks
Dual use
Support Vector Machines
Dual use
Unsupervised Learning Algorithms
Association Rules
Pattern detection
k-means clustering
Clustering
Meta-Learning Algorithms
Bagging
Dual use
Boosting
Dual use
Random Forests
Dual use
To begin applying machine learning to a real-world project, you will need to determine which of the four learning tasks your project represents: classification, numeric prediction, pattern detection, or clustering. The task will drive the choice of algorithm. For instance, if you are undertaking pattern detection, you are likely to employ association rules. Similarly, a clustering problem will likely utilize the k-means algorithm, and numeric prediction will utilize regression analysis or regression trees.
Torsten Hothorn maintains an exhaustive list of packages available in R for implementing machine learning algorithms.
Model evaluation
Whenever we are building a model, it needs to be tested and evaluated to ensure that it will not only work on trained data, but also on unseen data and can generate results with accuracy. A model should not generate a random result though some noise is permitted. If the model is not evaluated properly then the chances are that the result produced with unseen data is not accurate. Furthermore, model evaluation can help select the optimum model, which is more robust and can accurately predict responses for future subjects.
There are various ways by which a model can be evaluated:
Split test. In a split test, the dataset is divided into two parts, one is the training set and the other is test dataset. Once data is split the algorithm will use the training set and a model is created. The accuracy of a model is tested using the test dataset. The ratio of dividing the dataset in training and test can be decided on basis of the size of the dataset. It is fast and great when the dataset is of large size or the dataset is expensive. It can produce different result on how the dataset is divided into the training and test dataset. If the date set is divided in 80% as a training set and 20% as a test set, 60% as a training set and 40%, both will generate different results. We can go for multiple split tests, where the dataset is divided in different ratios and the result is found and compared for accuracy.
Cross validation. In cross validation, the dataset is divided in number of parts, for example, dividing the dataset in 10 parts. An algorithm is run on 9 subsets and holds one back for test. This process is repeated 10 times. Based on different results generated on each run, the accuracy is found. It is known as k-fold cross validation is where k is the number in which a dataset is divided. Selecting the k is very crucial here, which is dependent on the size of dataset.
Bootstrap. We start with some random samples from the dataset, and an algorithm is run on dataset. This process is repeated for n times until we have all covered the full dataset. In aggregate, the result provided in all repetition shows the model performance.
Leave One Out Cross Validation. As the name suggests, only one data point from the dataset is left out, an algorithm is run on the rest of the dataset and it is repeated for each point. As all points from the dataset are covered it is less biased, but it requires higher execution time if the dataset is large.
Model evaluation is a key step in any machine learning process. It is different for supervised and unsupervised models. In supervised models, predictions play a major role; whereas in unsupervised models, homogeneity within clusters and heterogeneity across clusters play a major role.
Some widely used model evaluation parameters for regression models (including cross validation) are as follows:
Coefficient of determination
Root mean squared error
Mean absolute error
Akaike or Bayesian information criterion
Some widely used model evaluation parameters for classification models (including cross validation) are as follows:
Confusion matrix (accuracy, precision, recall, and F1-score)
Gain or lift charts
Area under ROC (receiver operating characteristic) curve
Concordant and discordant ratio
Some of the widely used evaluation parameters of unsupervised models (clustering) are as follows:
Contingency tables
Sum of squared errors between clustering objects and cluster centers or centroids
Silhouette value
Rand index
Matching index
Pairwise and adjusted pairwise precision and recall (primarily used in NLP)
Bias and variance are two key error components of any supervised model; their trade-off plays a vital role in model tuning and selection. Bias is due to incorrect assumptions made by a predictive model while learning outcomes, whereas variance is due to model rigidity toward the training dataset. In other words, higher bias leads to underfitting and higher variance leads to overfitting of models.
In bias, the assumptions are on target functional forms. Hence, this is dominant in parametric models such as linear regression, logistic regression, and linear discriminant analysis as their outcomes are a functional form of input variables.
Variance, on the other hand, shows how susceptible models are to change in datasets. Generally, target functional forms control variance. Hence, this is dominant in non-parametric models such as decision trees, support vector machines, and K-nearest neighbors as their outcomes are not directly afunctional form of input variables. In other words, the hyperparameters of non-parametric models can lead to overfitting of predictive models.
Artificial Intelligence (AI) has seen explosive growth in recent years, and the development of different types of Machine Learning (ML) has been a driving force behind it. The numbers speak for themselves: According to McKinsey, private equity and venture-capital funding in AI companies increased nearly fivefold from $16 billion in 2015 to $79 billion in 2022. It’s clear that businesses are eager to adopt AI/ML and explore its potential. However, with so many different types of machine learning available, it can be challenging to understand which one is best suited for a particular application. In this article, let’s take a closer look at the four main types of machine learning and their respective applications: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
1. Supervised Learning
Supervised learning involves using labeled datasets to train algorithms for accurate classification or outcome prediction. During training, machines use labeled data to predict output in the future. The labeled data helps set a strategic path for machines as they map inputs to the output. Additionally, analysts use test datasets to check the accuracy of the analysis after training continuously. Various industries such as healthcare, finance, and marketing widely use supervised learning
Types of Supervised Learning
Supervised machine learning can be classified into two types of problems: classification and regression. Classification algorithms are utilized when the output variable is binary or categorical, making them ideal for identifying whether emails are spam or legitimate. On the other hand, regression algorithms are utilized to make predictions like weather and market conditions for problems that involve a linear relationship between the input and output variables.
Applications of Supervised Learning
Medical diagnosis
Fraud detection
Spam detection
Speech recognition
2. Unsupervised Learning
Unsupervised learning is one of the four main types of machine learning techniques. It utilizes unlabeled and unclassified datasets to make predictions without human intervention. This method is useful for categorizing or grouping unsorted data based on similarities and differences, as well as discovering hidden patterns and trends in the input data. Unlike supervised learning, unsupervised learning algorithms learn from the data without any fixed output variable, making them suitable for complex tasks. They identify hidden patterns or groupings in the data; this makes them ideal for clustering, anomaly detection, and exploratory data analysis.
Types of Unsupervised Learning
Unsupervised learning can be classified into two main techniques: clustering and association. Cluster analysis is an unsupervised learning technique that involves grouping data points based on their similarities, differences, or features without prior knowledge or labels. Businesses commonly use this technique in applications such as retail marketing, email marketing, and streaming services to identify similarities and patterns.On the other hand, association is an unsupervised learning technique where machines discover interesting relations and connections among variables in large input datasets. Businesses commonly use associations for applications such as web usage mining, plagiarism checking, developing recommendations, and continuous production.
Applications of Unsupervised Learning
Anomaly detection to identify fraud transactions
Forecasting and predictive modeling
Identifying and targeting market segments
Recommendation systems to suggest products or services
3. Semi-Supervised Learning
Semi-supervised learning is a highly efficient and cost-effective machine learning technique combining labeled and unlabeled data during training. It allows machines to learn from all the available data by utilizing both supervised and unsupervised learning advantages. This method first uses unsupervised learning algorithms to group similar data. Thereafter, this labels previously unlabeled data, making the dataset more robust for training. Additionally, semi-supervised learning enables machines to improve their accuracy and performance.
Types of Semi-Supervised Learning
Semi-supervised machine learning includes self-supervised learning and multiple-instance learning. The self-supervised learning technique frames the problem as a supervised learning task to generate labeled data from unlabeled data. This approach is particularly useful when obtaining labeled data is relatively inexpensive. Additionally, it has shown impressive results in various applications. Notably, Google and Facebook utilize self-supervised techniques in computer vision and natural language processing.On the other hand, multiple-instance learning provides weakly supervised learning where bags containing training instances are labeled instead of individual instances. This approach allows leveraging weakly labeled data often present in business problems due to the high cost of labeling. Furthermore, it is useful in scenarios where only partial information is available and has been applied in various fields, including medical diagnosis, image classification, and natural language processing.
Applications of Semi-Supervised Learning
Medical image analysis
Speech recognition and natural language processing
Text classification and sentiment analysis
Fraud detection in finance
4. Reinforcement Learning
Reinforcement learning is a machine learning technique where an agent learns to take optimal actions through environmental feedback. Unlike other types of machine learning, this technique does not rely on labeled data. Instead, it utilizes a trial-and-error approach with a feedback-based process that allows the agent to learn from its experiences. One of the main advantages of reinforcement learning is its ability to learn from experience and improve performance over time.
Types of Reinforcement Learning
Reinforcement learning includes positive reinforcement learning and negative reinforcement learning. Positive reinforcement learning is a type of learning where the agent is rewarded for taking actions that lead to positive outcomes. The reward can be a numerical value, such as a score or a probability, or a symbolic value, such as a label or a tag. Negative reinforcement learning, on the other hand, is a type of learning where the agent is punished for taking actions that lead to negative outcomes. Here, the agent learns to avoid actions that result in punishment and instead takes actions that lead to positive outcomes.
Applications of Reinforcement Learning
Building intelligent robotics
Developing personalized treatment plans for patients
Autonomous vehicles
Game playing
ALSO READ: What is Classification in Machine Learning and Why is it Important?
Frequently Asked Questions
How Does Reinforcement Learning Differ from Supervised and Unsupervised Learning?
Reinforcement learning involves an agent learning to take actions that maximize a reward signal provided by the environment. Therefore, this learning process enables the agent to develop a policy that maps states to actions to achieve its goal. In contrast, supervised learning requires labeled data to learn how to make predictions, while unsupervised learning attempts to find patterns or structures in unlabeled data without any feedback signal.
What Types of Algorithms Fall Under the Category of Supervised Learning?
Common supervised learning algorithms include Linear Regression, Logistic Regression, Random Forest, Decision Trees, Naive Bayes, and Neural Networks. These perform various tasks such as regression, classification, and prediction, using labeled data for training.
What is Deep Learning, and How Does it Fit into the Different Types of Machine Learning?
Machine learning and deep learning are forms of AI. Specifically, machine learning is an AI that can learn and adapt automatically with minimal human intervention. On the other hand, deep learning is a subcategory of machine learning that uses artificial neural networks to imitate the human brain’s learning process.
ALSO READ: How AI is Changing Cybersecurity: New Threats & Opportunities
To conclude, it is evident that these types of machine learning are crucial components of the ever-growing digital world. As technology advances, the demand for professionals specializing in machine learning will only increase. If you’re interested in pursuing a career in this field, explore the machine learning and artificial intelligence courses Emeritus offers in collaboration with some of the world’s best universities.
Imagine asking a computer to identify a picture of a cat or a dog without any supporting data; the computer has a 50/50 chance of being correct in this scenario. Now, imagine writing a program that teaches a computer how to actively learn the difference between the animals by analyzing photos of both.
This is the essence of machine learning. As humans, we learn from experience while machines generally follow our instructions. However, with machine learning, we can train computers to learn from data and perform high-level analyses and predictions. Machine learning is one of modern technology’s most promising concepts, one with boundless applications across most industries.
If you’re interested in a technology-related career, there’s a good chance that a working knowledge of machine learning will make you more marketable. In fact, some jobs focus specifically on incorporating machine learning advancements in order to help businesses gain a competitive advantage
What Is Machine Learning?
The simplest machine learning definition is this: the science of teaching computers how to learn like humans. Machine learning requires algorithms to examine huge datasets, find patterns within that data, and then make assessments and predictions based on those patterns. Essentially, it is a branch of artificial intelligence (AI) that shifts the rules of programming as we conventionally understand them.
Normally, programmers write programs where they input data and rules and the computer follows those rules to produce an answer. With machine learning, programmers input the data and the answer, and the computer determines the rules for producing that answer. In the earlier pet picture example, programmers would input the answer (“This is a photo of a cat”), the data (photos of cats and dogs), and the computer would use an algorithm to learn the difference.
Machine learning is applied in many familiar ways. Your favorite streaming service uses machine learning to recommend movies and shows based on your viewing habits; financial institutions use it to spot fraud in billions of transactions and devise ways to prevent it; self-driving cars use it to learn directional commands; and phones use it to enact accurate facial recognition.
According to a 2020 study, the global size of the machine learning market was valued at $6.9 billion in 2018. It is projected to increase nearly 44 percent through 2025 as companies seek to optimize their supply chains and use more digital resources to reach customers.
To be effective, machine learning needs detailed pieces of data from diverse sources. Algorithms learn best when they can apply vast amounts of data to a specific model. For example, the more photos of dogs and cats you input, the better the algorithm will become in identifying the differences between the animals.
The term “machine learning” is often used synonymously with artificial intelligence and, while these concepts share similarities, they are generally used for different purposes. AI is the broad science of training machines to perform human tasks, while machine learning is one of many AI-based methods of accomplishing that training.
Machine Learning Algorithm Types
Algorithms are the procedures that computers use to perform pattern recognition on data models and create an output. Many types of algorithms exist, and they fall into four primary groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Supervised Learning
Supervised learning is a process where labeled input data and the correct answers are both given to a computer so that it can learn how to reach the correct answer on its own. The correct answer, or output, can refer to an object, a situation, or a problem that needs to be solved.
There are two types of supervised learning: classification and regression. Classification is simply the process of sorting identified data into groups. To illustrate, let’s apply classification to our cats and dogs example: The programmer inputs labeled photos of cats and dogs so the computer knows which photo shows which type of animal. Using that training data set to learn how to identify the pictures, the computer can then apply its knowledge to a new data set and label them correctly. The more photos the computer analyzes, the faster and more accurate it becomes at classifying the data.
The second type of supervised learning is regression, which enables the computer to forecast likely future or desirable outcomes from a labeled data set. Different types of regression are used to forecast future sales, anticipated stock performanced, or the impact of financial events on the global economy. However, regression can be used for far more than financial analysis.
Here are some other examples:
Social Media
Facebook offers you a friend suggestion because it recognizes your friend in a photo album of tagged pictures.
Streaming Suggestions
Netflix recommends movies for you to watch based on your past viewing choices, ratings, and the viewing habits of subscribers with similar tastes.
Predicting Home Prices
Realtors use machine learning to analyze housing data (location, square footage, number of bedrooms, added features like pools, etc.) in comparison to other properties to understand not only how to best price a property, but also how that price will impact days on the market and likely closing dates for their clients.
Suggesting Further Purchases
When you check out a cart full of items at the grocery store, those purchases become data. Retailers use that data in many ways — such as predicting future purchases, making suggestions, or offering coupons as incentives.
Unsupervised Learning
Unsupervised learning refers to the process in which a computer finds nonintuitive patterns in unlabeled data. It’s different from supervised learning because the datasets are not labeled and the computer is not given a specific question to answer.
There are many different types of unsupervised learning including K-means clustering, hierarchical clustering, anomaly detection, and principal component analysis to name a few. The most commonly discussed uses are clustering and anomaly detection.
Clustering is used to find natural groups, or clusters, within a dataset. These clusters can be analyzed to group like customers together (e.g, customer segmentation), identify products that are purchased at the same time (e.g., peanut butter and jelly), or better understand the attributes of successful executives (e.g., technical skills, personality profile, education).
In our dogs and cats example, assume you input pictures of dogs and cats but don’t label them. Using clustering, the computer will look for common traits (body types, floppy ears, whiskers, etc.) and group the photos. However, while you may expect the computer to group the photos by dogs vs. cats, it could group them by fur color, coat length, or size. The benefit of clustering is that the computer will find nonintuitive ways of looking at data which enable the discovery of new data trends (e.g., there are twice as many long-coated animals as short-coated) which allow for new marketing opportunities (e.g., dry pet shampoo and brush marketing increases).
In anomaly detection, however, the computer looks for rare differences rather than commonalities. For example, if we used anomaly detection on our dog and cat photos, the computer might flag the photo of a Sphynx cat because it is hairless or an albino dog due to its lack of color.
Here are some other applications of anomaly detection.
Finding Fraud
Banks analyze all sorts of transactions: deposits, withdrawals, loan repayments, etc. Unsupervised learning can group these data points and flag outlier transactions (e.g., transactions that don’t align with the majority of data points) that may indicate fraud.
Consumer Studies
Companies use anomaly detection to identify and understand actions competitors may take in the marketplace. For example, a retailer may expect to take three share points in every new market they open a store during the first month of operations; however, they may notice certain new stores are underperforming and don’t know why. Anomaly detection can be used to identify likely competitive activity which is preventing share growth. Specifically, the anomaly of common products not being found in their shoppers’ baskets (e.g., bread, milk, eggs, chicken breast) which may indicate covert competitor incentives that are successfully impacting the retailer’s shopper frequency and average order size.
Image Recognition
Computers use unsupervised learning to perform all sorts of image recognition tasks including facial recognition to open your mobile phone and healthcare imaging where identifying cell-structure anomalies can assist in cancer diagnosis and treatment.
Semi-Supervised Learning
Semi-supervised learning is essentially a combination of supervised and unsupervised learning techniques. It merges a small amount of manually labeled data (a supervised learning element) as a basis for autonomously defining a large amount of unlabeled data (an unsupervised element). Through data clustering, this method makes it possible to train a machine learning algorithm (ML algorithm) on data annotation (e.g., the labeling or classification of data) without manually labeling all of the training data first, potentially increasing efficiency without sacrificing quality or accuracy.
For example, if you have a large data set consisting of dogs and cats, a semi-supervised approach would allow you to manually label a small portion of that data (identifying a few pictures as “dogs” and a few others as “cats”), and the ML algorithm would then be equipped to properly define the remaining data. This blends the benefits of supervised and unsupervised learning by nudging the algorithm to make strong autonomous decisions with less initial human oversight.
Image Classification
While higher-level image classification often requires a fully supervised approach (due to the necessary labeling of a large amount of initial training data), specific image classification scenarios can benefit from semi-supervised learning. For example, to annotate images of handwritten numbers, training data must be clustered to include the most representative variations of the written numbers and can then be used to inform the ML algorithm. In turn, the algorithm should be able to identify unlabeled images of handwritten numbers with relatively high accuracy, yielding the intended outcome with less initial oversight.
Document Classification
Similarly, semi-supervised learning can be useful in document classification, eliminating the need for human workers to read through numerous text documents just to broadly classify them. A semi-supervised approach allows the algorithm to learn from a relatively small amount of text data so that it can identify and classify the larger amount of unlabeled documents.
Reinforcement Learning
Reinforcement learning is the process by which a computer learns how to behave in a certain environment by performing an action and seeing a specific result. In this process, the key terms to know are agents and environments. Agents interact with the environment through actions and receive feedback regarding those actions. Consider it similar to the first time you (the agent) touched a hot stove (the environment) — the feedback from the action (e.g., pain of touching the stove) reinforced the idea that you shouldn’t touch a hot stove again.
Reinforcement can also be applied to our cats and dogs scenario. If you input an image of a dog and the computer says it’s a cat, you can then correct that answer. The computer will learn from that correction, or reinforcement, and increase its ability to properly identify the image over time and through repetition of the process.
Reinforcement is a growing method of machine learning because of its applications in robotics and automation. Consider these examples:
Self-Driving Cars
Autonomous vehicles interpret a huge amount of data through cameras, sensors, and radar that monitor their surroundings. Reinforcement learning contributes to the real-time decision-making process.
Industry Automation
Companies automate tasks in warehouses and production facilities through robotics that operate on reinforcement learning models.
Healthcare
Reinforcement learning is becoming more common in medicine because its methodology (e.g., learning from interactions in an environment) often mirrors that of diagnosis and treating diseases.
Gaming
Reinforcement learning algorithms are popular for video games because they learn quickly and can mimic human performance. Reinforcement is one way computers learn how to master games from chess to complex video games, allowing bot players to engage with human players in a realistic way.
Machine Learning Jobs
As we look to automate more processes at work and in our daily lives, machine learning will become more valuable. Machine learning is important to data science, artificial intelligence, and robotics (among many other fields).
Where can knowledge of machine learning take you? Here are a few potential careers to consider.
Machine Learning Engineer: Though coding is required, this role is a bit different than that of a computer programmer. Machine learning engineers build programs that teach computers how to identify patterns and perform tasks based on those patterns. This is an ideal career path for those who want to get into robotics.
Machine Learning Data Scientist: Data scientists combine statistics, programming, and data analysis to generate insight from data — a skill that is in high demand. According to the U.S. Bureau of Labor Statistics (BLS), the demand for computer and information research scientists is expected to grow by 15 percent through 2029. Machine learning is an important component of becoming a data scientist.
NLP Scientist: When you ask Siri or Alexa a question, they answer because of natural language processing (NLP). NLP scientists work in a unique world of textual data analysis, linguistics, and computer programming to facilitate communication between humans and machines.
Business Intelligence Developer: Companies need ways to harness, assess, and report all the data they collect, and business intelligence (BI) provides that framework. BI developers work with data warehouses, visualization software, and other tools to explain what is happening. BI is also vital in generating ways to benefit from consumer data.
Data Analyst: Want to help companies and organizations make sense of their data? Then you may want to consider becoming a data analyst. You’ll learn how to mix statistics, business knowledge, and communication skills to bring data to life. As data generation grows, so do the job prospects for analysts. The BLS projects a 25 percent increase by 2029.
Want to learn more about the function of machine learning in data science? Check out this guide to understanding data science roles.
Machine Learning FAQs
What is machine learning like for beginners?
Machine learning requires a solid foundation in fields like math, statistics, and programming. Calculus and linear algebra are important starting points, as is the ability to code. A great way to learn about machine learning, and other data science skills, is to enroll in a data science boot camp.
What is a good introduction to machine learning?
What is an example of machine learning?
Ready to take the next step in a career that involves machine learning? Consider a bootcamp in data science and analytics. The 24-week online program at Georgia Tech Data Science and Analytics Boot Camp is a great way to learn in-demand skills to get you ready for your job search. Contact us today to get started.
Machine learning (ML) is a type of algorithm that automatically improves itself based on experience, not by a programmer writing a better algorithm. The algorithm gains experience by processing more and more data and then modifying itself based on the properties of the data.
Types of machine learning
There are many varieties of machine learning techniques, but here are three general approaches:
reinforcement learning: The algorithm performs actions that will be rewarded the most. Often used by game-playing AI or navigational robots.
unsupervised machine learning: The algorithm finds patterns in unlabeled data by clustering and identifying similarities. Popular uses include recommendation systems and targeted advertising.
supervised machine learning: The algorithm analyzes labeled data and learns how to map input data to an output label. Often used for classification and prediction.
Let’s dive into one of the most common approaches to understand more about how a machine learning algorithm works.
Neural networks
An increasingly popular approach to supervised machine learning is the neural network. A neural network operates similarly to how we think brains work, with input flowing through many layers of “neurons” and eventually leading to an output.
Diagram of a neural network, with circles representing each neuron and lines representing connections between neurons. The network starts on the left with a column of 3 neurons labeled “Input”. Those neurons are connected to another column of 4 neurons, which itself connects to another column of 4, and those neurons are labeled “Hidden layers”. The second hidden layer of neurons is connected to a column of 3 neurons labeled “Output”.
Training a network
Computer programmers don’t actually program each neuron. Instead, they train a neural network using a massive amount of labeled data.
The training data depends on the goal of the network. If its purpose is to classify images, a training data set could contain thousands of images labeled as “bird”, “airplane”, etc.
A grid of images in 10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).
Images from the CIFAR10 training data set. Image source: CIFAR10
The goal of the training phase is to determine weights for the connections between neurons that will correctly classify the training data.
A diagram of a neural network classifying an image of a plane. Parts of the image are fed into the first layer of neurons, those neurons lead to a middle layer, and those neurons lead to a final layer of neurons. Each edge between neurons is labeled with a question mark, denoting an unknown weight.
The weights between the neurons are unknown (labeled with a “?” here), and the neural network wants to find weights that will result in classifying each image correctly.
The neural network starts off with all the weights set to random values, so its initial classifications are way off. It learns from its mistakes, however, and eventually comes up with a set of weights that do the best job at classifying all of the training data.
A diagram of a neural network classifying an image of a plane. Parts of the image are fed into the first layer of neurons, those neurons lead to a middle layer, and those neurons lead to a final layer of neurons. Each neuron has a weight (from 0 to 1). In the final layer, the neuron labeled “plane” has the highest weight.
Each of the connections between neurons is assigned a weight (represented by shades of green). A neuron multiplies each connection weight by the value of the input neuron, and sums up all of those to come up with a single number (shown on each neuron). The neuron will only send its value to the next layer if it’s above a threshold.
Using the network
When the neural network is asked to classify an image, it uses the learned weights and outputs the possible classes and their probabilities.
Diagram of a neural network, with circles representing each neuron and lines representing connections between neurons. The network starts on the left with an image of a fox. The image is broken into 4 parts, and those parts are connected to column of 4 neurons, which itself connects to another column of 4. The second column is connected to 3 possible outputs: “Fox (0.85)”, “Dog (0.65)”, and “Cat (0.25)”.
Accuracy
The accuracy of a neural network is highly dependent on its training data, both the amount and diversity. Has the network seen the object from multiple angles and lighting conditions? Has it seen the object against many different backgrounds? Has it really seen all varieties of that object? If we want a neural network to truly understand the world, we need to expose it to the huge diversity of our world.
Companies, governments, and institutions are increasingly using machine learning to make decisions for them. They often call it “artificial intelligence,” but a machine learning algorithm is only as intelligent as its training data. If the training data is biased, then the algorithm is biased. And unfortunately, training data is biased more often than it’s not.
In the following articles, we’ll explore the ramifications of letting machines make decisions for us based on biased data.
Artificial intelligence is rapidly advancing and becoming more prevalent in our daily lives. From chatbots to autonomous vehicles, AI is changing the way we interact with technology.
But creating AI-powered systems is not a simple task. It requires a specialized skill set to build AI platforms.
That’s where AI engineering comes in.
Defining AI Engineering
AI engineering is the process of designing and developing intelligent systems and applications that can learn and adapt from data. AI engineers use various techniques such as machine learning, deep learning, natural language processing, and reinforcement learning to build intelligent systems that can perform complex tasks, such as image recognition, speech recognition, text analysis, and decision-making.
Machine Learning vs AI Engineering
You might be wondering, “Wait, isn’t that just machine learning?” Fair question. Because AI is an evolving field, the exact definitions of and relationship between the machine learning and AI engineering disciplines can be confusing.
Generally speaking, machine learning and AI engineering are two related but distinct fields within the broader field of artificial intelligence. You can also think of machine learning as an important tool used by AI engineers to build intelligent systems, making it one part of the broader field of AI engineering.
Machine learning is a subfield of AI that focuses on developing algorithms that can learn from and make predictions or decisions on data without being explicitly programmed to do so. In other words, it is a set of techniques that enable machines to learn from data and improve their performance on specific tasks over time. Machine learning techniques include supervised learning, unsupervised learning, reinforcement learning, and deep learning.
AI engineering, on the other hand, is the broader field that encompasses the development, deployment, and maintenance of AI systems. It involves applying the principles of software engineering to build robust, scalable, and maintainable AI systems. AI engineering includes designing and implementing AI models, deploying them in production environments, and managing the data infrastructure needed to support the models. It also involves ensuring that the AI system is secure, reliable, and efficient.
In essence, machine learning is a subfield of AI that focuses on the development of algorithms that enable machines to learn from data, while AI engineering is the application of software engineering principles to build and deploy AI systems.
How AI Engineering Works
The process of AI engineering typically involves the following stages:
Problem identification: The purpose of any AI model is to solve a business or consumer problem. To develop a successful AI model, engineers start by identifying the exact pain point the AI will solve. For example, many knowledge workers have trouble keeping up with tight production schedules. Conversational AI models like ChatGPT solve this problem by automating labor intensive tasks like coding, writing, and research.
Data collection: AI engineering begins with the collection of relevant data from various sources. The data can be in different formats such as text, images, audio, or video. The quality of the data is crucial as it affects the accuracy of the AI-powered system.
Data preprocessing: The collected data needs to be cleaned and preprocessed to remove any noise or inconsistencies. The data is then transformed into a format that can be used for training and building AI models.
Algorithm Development: AI engineers use various machine learning algorithms such as regression, clustering, classification, and neural networks to build intelligent systems. The choice of algorithm depends on the nature of the problem and the type of data being used.
Model Training: Once the algorithm is selected, the next stage involves training the model using the preprocessed data. The model is trained using various optimization techniques to improve its accuracy and performance.
Model Evaluation: After the model is trained, it needs to be evaluated to ensure that it performs well on new data. AI engineers use various evaluation metrics such as precision, recall, and F1 score to assess the performance of the model.
Deployment: The final stage of AI engineering involves deploying the model in a production environment. The model needs to be integrated with other systems and tested to ensure that it works correctly.
AI Engineering Skills
AI engineering is a multidisciplinary field that requires expertise in various areas such as computer science, mathematics, statistics, and domain knowledge. Here are some of the essential skills required to become an AI engineer.
Programming
AI engineers need to have a good understanding of programming languages such as Python, R, and Java, as well as machine learning libraries like TensorFlow and PyTorch.
Mathematics and Statistics
A strong foundation in mathematics and statistics is essential for AI engineering. AI engineers need to be proficient in calculus, linear algebra, probability, and statistics.
Machine Learning
AI engineers need to have a deep understanding of machine learning algorithms and techniques such as deep learning, supervised and unsupervised learning, reinforcement learning, and neural networks.
Data Analysis and Management
AI engineers need to be skilled in data analysis and management, including data cleansing, pre-processing, feature extraction, and data visualization.
Problem-Solving Skills
AI engineers must be adept at problem-solving and troubleshooting. They need to be able to analyze complex problems, identify potential solutions, and test and implement the best approach.
Communication Skills
AI engineers need to be able to communicate technical concepts and ideas to both technical and non-technical stakeholders.
Creativity
AI engineers must be creative and have a strong imagination to come up with innovative solutions to complex problems.
Domain Knowledge
AI engineers must have domain knowledge in the area they are working on. For example, a healthcare AI engineer would have knowledge of healthcare processes and regulations.
Continuous Improvement
AI is a fast-evolving field, so AI engineers need to be committed to continuous learning to keep up with new technologies and techniques.
Collaboration Skills
AI engineering is often a team effort, so AI engineers need to be able to work collaboratively with other team members, such as data scientists, product managers, and business stakeholders.
Conclusion
AI engineering is an exciting field that is rapidly evolving and has immense potential to transform various industries. It requires a multidisciplinary skill set, and AI engineers should be proficient in programming, machine learning, data management, deep learning, and deployment. With the increasing demand for AI-powered systems, AI engineering is set to become one of the world’s most sought-after skills.
Why are there so many machine learning techniques? The thing is that different algorithms solve various problems. The results that you get directly depend on the model you choose. That is why it is so important to know how to match a machine learning algorithm to a particular problem.
In this post, we are going to talk about just that. Let’s get started.
Variety of machine learning techniques
First of all, to choose an algorithm for your project, you need to know about what kinds of them exist. Let’s brush up your knowledge of different classifications.
Algorithms grouped by learning style
It’s possible to group the algorithms by their learning style.
Supervised learning
In the case of supervised learning, machines need a “teacher” who “educates” them. In this case, a machine learning specialist collects a set of data and labels it. Then, they need to communicate the training set and the rules to the machine. The next step is to watch how the machine manages to process the testing data. If there are some mistakes made, the programmer corrects them and repeats the action until the algorithm works accurately.
Unsupervised learning
This type of machine learning doesn’t require an educator. A computer is given a set of unlabeled data. It is supposed to find the patterns and come up with insights by itself. People can slightly guide the machine along the process by providing a set of labeled training data as well. In this case, it is called semi-supervised learning.
Reinforcement learning
Reinforcement learning happens in an environment where the computer needs to operate. The environment acts as the teacher providing the machine with positive or negative feedback that is called reinforcement.
You can find a more detailed explanation about these techniques in our post on the difference between AI and machine learning.
Machine learning techniques grouped by problem type
Another way to divide the techniques into groups is based on the issues they solve.
In this section, we will talk about classification, regression, optimization, and other groups of algorithms. We are also going to have a look at their use in industry. For more detailed information about every common machine learning algorithm, check out our post about machine learning algorithm classification.
Common algorithms
Here are the most popular ML algorithms. Sometimes they belong to more than one group because they are effective at solving more than one problem.
Logistic Regression,
Linear Regression
Decision Tree
SVM
Naive Bayes
k-NN
K-Means
Neural networks
Random Forest
Dimensionality Reduction Algorithms
Gradient Boosting algorithms
To help you orient yourself in the post, use this pic. It features common algorithms that we are going to talk about in this post.
Classification
Classification helps us to deal with a wide range of problems. It allows us to make more informed decisions, sort out spam, predict whether the borrower will return the loan, or tag friends in a Facebook picture.
These algorithms predict discrete variable labels. A discrete variable has a countable number of possible values and can be classified. The accuracy of the prediction depends on the model that you choose.
Imagine that you develop an algorithm that predicts whether a person has or does not have cancer. In this case, the model that you choose should be very precise in predicting the result.
Typical classification algorithms are logistic regression, Naive Bayes, and SVM. More information about them and other algorithms you can find in our blog.
Clustering
Sometimes you need to divide things into categories but you don’t know what these categories are. Classification uses predefined classes to assign to objects. On the other hand, clustering allows to identify similarities between objects, and then group them according to the characteristics they have in common. This is the mechanics that lays behind detecting fraud, analyzing documents, grouping clients, and more. Clustering is widely used in sales and marketing for customer segmentation and personalized communication.
K-NN, k-means clustering, Decision trees, Random forest can all be used for clustering tasks.
Prediction
Trying to find out the relationship between two or more continuous variables is a typical regression task.
Note: If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable.
An example of such a task is predicting housing prices based on their size and location. The price of the house in this case is a continuous numerical variable.
Linear regression is the most common algorithm in this field. Multivariate regression algorithms, Ridge Regression, and LASSO regression are used when you need to model a relationship between more than two variables.
Optimization
Machine learning software enables you to provide a data-driven approach to continuous improvement in practically any field. You can apply product usage analytics in order to discover how the new product features affect demand. Sophisticated software equipped with empirical data helps to uncover ineffective measures, allowing you to avoid unsuccessful decisions.
For example, it is possible to use a heterarchical manufacturing control system in order to improve the capacity for a dynamic manufacturing system to adapt and self-manage. Machine learning techniques uncover the best behavior in various situations in real-time – which leads to continuous improvement of the system.
Gradient descent algorithms are generally used in ML to work with optimization.
Anomaly detection
Financial institutions lose about 5% of revenue each year to fraud. By building models based on historical transactions, social network information, and other sources of data, it is possible to spot anomalies before it’s too late. This helps detect and prevent fraudulent transactions in real-time, even for previously unknown types of fraud.
Typical anomaly detection algorithms are SVM, LOF, k-NN, k-means.
Ranking
You can apply machine learning to build ranking models. Machine learning ranking (MLR) usually involves the application of supervised, semi-supervised, or reinforcement algorithms. An example of a ranking task is search engine systems like SearchWiki by Google.
Examples of ranking algorithms are RankNet, RankBoost, RankSVM, and others.
Recommendation
Recommender systems offer valuable suggestions to users. This method brings utility to users and also benefits the companies because it motivates their clients to buy more or explore more content.
Items are ranked according to their relevance. The most relevant ones are displayed to the user. The relevancy is determined based on historical data. You know how it works if you’ve ever watched anything on Youtube or Netflix. The systems offer you similar videos to what you have already watched.
The main algorithms used for recommender systems are collaborative filtering algorithms and content-based systems.
How to choose machine learning techniques to solve your problem
How to find the best machine learning algorithm for your problem? There are three basic approaches you can use.
Task-based learning
Categorize your problem. It’s possible to categorize tasks by input and by output.
By input:
If you have a set of labeled data or can prepare such a set, it is the domain of supervised learning.
If you still need to define a structure, it’s an unsupervised learning problem.
If you need the model to interact with an environment, you will apply a reinforcement learning algorithm.
By output:
If the output of the model is a number, it’s a regression problem.
If the output of the model is a class and the number of expected classes is known, it’s a classification problem.
If the output of the model is a class but the number of expected classes is unknown, it’s a clustering problem.
If you need to improve performance, it’s optimization.
If you want a system to offer options based on the history of actions, it’s a recommendation problem.
If you want to obtain insights from data, apply pattern recognition models.
If you want to detect problems, use anomaly detection algorithms.
Understand your data
The process of choosing the algorithm isn’t limited to categorizing the problem. You also need to have a closer look at your data because it plays an important role in the selection of the right algorithm for the problem. Some algorithms function normally with smaller sample sets while others require a huge number of samples. Certain algorithms work with categorical data while others only work with numerical input.
Understanding your data demands certain steps:
Processing. The components of data processing are pre-processing, profiling, cleansing, pulling together data from different internal and external sources.
Feature engineering. You need to transform raw data into features that can represent the underlying problem to the predictive models. It helps to improve accuracy and get the desired results faster.
Choosing the algorithm is a comprehensive task that demands the analysis of a variety of factors.
Other things that might affect the choice of a model:
Accuracy of the model;
Interpretability of the model;
Complexity of the model;
Scalability of the model;
Time it takes to build, train, and test the model;
Time it takes to make predictions using the model;
If the model meets your business goals.
Trial and error approach
Sometimes the problem is too complex and you do not know where to start. More than one model seems like a good fit, and it is difficult to predict which one will turn out the most effective. In this case, you can test a couple of models and assess them.
Set up a machine learning pipeline. It will compare the performance of each algorithm on the dataset based on your evaluation criteria. Another approach is to divide your data into subsets and use the same algorithm on different groups. The best solution for this is to do it once or have a service running that does this in intervals when new data is added.
Neural networks
Finally, the majority of tasks ML has to solve today can be solved with the help of neural networks. So, the final approach to choosing an ML model is just to always go for artificial neural networks.
However, these models are expensive and time-consuming to build, which is why other models still exist. Neural networks need extremely large databases in order to be accurate. Other types of ML techniques might not be as universal but solve assigned tasks effectively even when working with small datasets.
Moreover, they tend to overfit and are also hard to interpret – neural networks are basically black boxes, and researchers don’t know what’s happening inside.
So if you have a small budget, a small data sample, or aspire to get valuable insights that are easy to understand, NNs are not for you.
Final thoughts
Your results depend on whether you manage to select and build a successful ML model. If you have a machine learning project in mind and are looking for solutions, Serokell provides machine learning development services that suit your business goals. Contact us to learn how we can assist you with your project.
Building intelligent robotics
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