Machine Learning is defined as the study of computer programs that leverage algorithms and statistical models to learn through inference and patterns without being explicitly programed. Machine Learning field has undergone significant developments in the last decade.”
In this article, we explain machine learning, the types of machine learning and its applications in enterprise settings.
Table of Contents
What is Artificial Intelligence
What Is Machine Learning?
Types of Machine Learning
Applications of Machine Learning
Artificial Intelligence Vs. Machine Learning
Closing Thoughts for Techies
Machine Learning (ML) has proven to be one of the most game-changing technological advancements of the past decade. In the increasingly competitive corporate world, ML is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations .
The eventual adoption of machine learning algorithms and its pervasiveness in enterprises is also well-documented, with different companies adopting machine learning at scale across verticals.
Today, every other app and software all over the Internet uses machine learning in some form or the other. Machine Learning has become so pervasive that it has now become the go-to way for companies to solve a bevy of problems.
In this article, we’ll dive deeper into what machine learning is, the basics of ML, types of machine learning algorithms, and a few examples of machine learning in action. We will also take a look at the difference between artificial intelligence and machine learning.
What Is Artificial Intelligence?
To understand what machine learning is, we must first look at the basic concepts of artificial intelligence (AI). AI is defined as a program that exhibits cognitive ability similar to that of a human being. Making computers think like humans and solve problems the way we do is one of the main tenets of artificial intelligence.
AI exists as an umbrella term that is used to denote all computer programs that can think as humans do. Any computer program that shows characteristics, such as self-improvement, learning through inference, or even basic human tasks, such as image recognition and language processing, is considered to be a form of AI.
The field of artificial intelligence includes within it the sub-fields of machine learning and deep learning. Deep Learning is a more specialized version of machine learning that utilizes more complex methods for difficult problems. One thing to note, however, is the difference between machine learning and artificial intelligence. While machine learning is probabilistic (output can be explained, thereby ruling out the black box nature of AI), deep learning is deterministic.
The process of self-learning by collecting new data on the problem has allowed machine learning algorithms to take over the corporate space.
Learn More: 10 Most Common Myths About AI
What Is Machine Learning?
With machine learning algorithms, AI was able to develop beyond just performing the tasks it was programmed to do. Before ML entered the mainstream, AI programs were only used to automate low-level tasks in business and enterprise settings.
This included tasks like intelligent automation or simple rule-based classification. This meant that AI algorithms were restricted to only the domain of what they were processed for. However, with machine learning, computers were able to move past doing what they were programmed and began evolving with each iteration.
Machine learning is fundamentally set apart from artificial intelligence, as it has the capability to evolve. Using various programming techniques, machine learning algorithms are able to process large amounts of data and extract useful information. In this way, they can improve upon their previous iterations by learning from the data they are provided.
We cannot talk about machine learning without speaking about big data, one of the most important aspects of machine learning algorithms. Any type of AI is usually dependent on the quality of its dataset for good results, as the field makes use of statistical methods heavily.
Machine learning is no exception, and a good flow of organized, varied data is required for a robust ML solution. In today’s online-first world, companies have access to a large amount of data about their customers, usually in the millions. This data, which is both large in the number of data points and the number of fields, is known as big data due to the sheer amount of information it holds.
Big data is time-consuming and difficult to process by human standards, but good quality data is the best fodder to train a machine learning algorithm. The more clean, usable, and machine-readable data there is in a big dataset, the more effective the training of the machine learning algorithm will be.
As explained, machine learning algorithms have the ability to improve themselves through training. Today, ML algorithms are trained using three prominent methods. These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Learn More:Modern Machine Learning – Overview With Simple Examples
Types of Machine Learning
As with any method, there are different ways to train machine learning algorithms, each with their own advantages and disadvantages. To understand the pros and cons of each type of machine learning, we must first look at what kind of data they ingest. In ML, there are two kinds of data — labeled data and unlabeled data.
Labeled data has both the input and output parameters in a completely machine-readable pattern, but requires a lot of human labor to label the data, to begin with. Unlabeled data only has one or none of the parameters in a machine-readable form. This negates the need for human labor but requires more complex solutions.
There are also some types of machine learning algorithms that are used in very specific use-cases, but three main methods are used today.
Supervised learning is one of the most basic types of machine learning. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.
In supervised learning, the ML algorithm is given a small training dataset to work with. This training dataset is a smaller part of the bigger dataset and serves to give the algorithm a basic idea of the problem, solution, and data points to be dealt with. The training dataset is also very similar to the final dataset in its characteristics and provides the algorithm with the labeled parameters required for the problem.
The algorithm then finds relationships between the parameters given, essentially establishing a cause and effect relationship between the variables in the dataset. At the end of the training, the algorithm has an idea of how the data works and the relationship between the input and the output.
This solution is then deployed for use with the final dataset, which it learns from in the same way as the training dataset. This means that supervised machine learning algorithms will continue to improve even after being deployed, discovering new patterns and relationships as it trains itself on new data.
Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program.
In supervised learning, the labels allow the algorithm to find the exact nature of the relationship between any two data points. However, unsupervised learning does not have labels to work off of, resulting in the creation of hidden structures. Relationships between data points are perceived by the algorithm in an abstract manner, with no input required from human beings.
The creation of these hidden structures is what makes unsupervised learning algorithms versatile. Instead of a defined and set problem statement, unsupervised learning algorithms can adapt to the data by dynamically changing hidden structures. This offers more post-deployment development than supervised learning algorithms.
Reinforcement learning directly takes inspiration from how human beings learn from data in their lives. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method. Favorable outputs are encouraged or ‘reinforced’, and non-favorable outputs are discouraged or ‘punished’.
Based on the psychological concept of conditioning, reinforcement learning works by putting the algorithm in a work environment with an interpreter and a reward system. In every iteration of the algorithm, the output result is given to the interpreter, which decides whether the outcome is favorable or not.
In case of the program finding the correct solution, the interpreter reinforces the solution by providing a reward to the algorithm. If the outcome is not favorable, the algorithm is forced to reiterate until it finds a better result. In most cases, the reward system is directly tied to the effectiveness of the result.
In typical reinforcement learning use-cases, such as finding the shortest route between two points on a map, the solution is not an absolute value. Instead, it takes on a score of effectiveness, expressed in a percentage value. The higher this percentage value is, the more reward is given to the algorithm. Thus, the program is trained to give the best possible solution for the best possible reward.
Learn More: How Is AI Changing the Finance, Healthcare, HR, and Marketing Industries?
Applications of Machine Learning
Machine learning algorithms are used in circumstances where the solution is required to continue improving post-deployment. The dynamic nature of adaptable machine learning solutions is one of the main selling points for its adoption by companies and organizations across verticals.
Machine learning algorithms and solutions are versatile and can be used as a substitute for medium-skilled human labor given the right circumstances. For example, customer service executives in large B2C companies have now been replaced by natural language processing machine learning algorithms known as chatbots. These chatbots can analyze customer queries and provide support for human customer support executives or deal with the customers directly.
Machine learning algorithms also help to improve user experience and customization for online platforms. Facebook, Netflix, Google, and Amazon all use recommendation systems to prevent content glut and provide unique content to individual users based on their likes and dislikes.
Facebook utilizes recommendation engines for its news feed on both Facebook and Instagram, as well as for its advertising services to find relevant leads. Netflix collects user data and recommends various movies and series based on the preferences of the user. Google utilizes machine learning to structure its results and for YouTube’s recommendation system, among many other applications. Amazon uses ML to place relevant products in the user’s field of view, maximizing conversion rates by recommending products that the user actually wants to buy.
However, as ML continues to be applied in various fields and use-cases, it becomes more important to know the difference between artificial intelligence and machine learning.
Learn More: 10 Businesses Using Machine Learning In Innovative Ways
Artificial Intelligence Vs. Machine Learning
As American professor Douglas Hofstadter quotes, “AI is whatever hasn’t been done yet.” This is referred to as the AI Effect, wherein new techniques not only obsolete previous ones but also make the latter much more accessible and optimized for use. By this logic, artificial intelligence refers to any advancement in the field of cognitive computers, with machine learning being a subset of AI.
Today, the term ‘artificial intelligence’ has been used as more of an umbrella term to denote technology that exhibits human-like cognitive characteristics. As a rule of thumb, research in AI is moving towards a more generalized form of intelligence, similar to the way toddlers think and perceive the world around them. This could mark the evolution of AI from a program purpose-built for a single ‘narrow’ task to a solution deployed for ‘general’ solutions; the kind we can expect from humans.
Machine learning, on the other hand, is an exclusive subset of AI reserved only for algorithms that can dynamically improve on themselves. They are not statically programmed for one task like many AI programs are, and can be improved even after they are deployed. This not only makes them suitable for enterprise applications, but it is also a novel way to solve problems in an always-changing environment.
Machine learning also includes deep learning, a specialized discipline that holds the key to the future of AI. Deep learning features neural networks, a type of algorithm that is based on the physical structure of the human brain. Neural networks seem to be the most productive path forward for AI research, as it allows for a much closer emulation of the human brain than has ever been seen before.
Learn More: 10 Experts on the Future of AI
Closing Thoughts for Techies
Understanding the basics of machine learning and artificial intelligence is a must for anyone working in the tech domain today. Due to the pervasiveness of AI in today’s tech world, working knowledge of this technology is required to stay relevant.
Corporates are now in the middle of the adoption curve for artificial intelligence, mainly due to accessible cloud platforms and exponential advancements in the field. This makes AI an interesting career opportunity for those who have the capability and experience to take it up. Since this field functions as a combination of statistics, computer science, and logical thinking, it is varied in what it can offer to new entrants. Moreover, a variety of positions such as data scientists, machine learning engineers, and AI developers offer choices to aspirants across verticals.
Machine learning is a field of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed. It has become an increasingly popular topic in recent years due to the many practical applications it has in a variety of industries. In this blog, we will explore the basics of machine learning, delve into more advanced topics, and discuss how it is being used to solve real-world problems. Whether you are a beginner looking to learn about machine learning or an experienced data scientist seeking to stay up-to-date on the latest developments, we hope you will find something of interest here.
What is Machine Learning?
Machine learning is an application of artificial intelligence that uses statistical techniques to enable computers to learn and make decisions without being explicitly programmed. It is predicated on the notion that computers can learn from data, spot patterns, and make judgments with little assistance from humans.
It is a subset of Artificial Intelligence. It is the study of making machines more human-like in their behavior and decisions by giving them the ability to learn and develop their own programs. This is done with minimum human intervention, i.e., no explicit programming. The learning process is automated and improved based on the experiences of the machines throughout the process.
Good quality data is fed to the machines, and different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand and the type of activity that needs to be automated.
Now you may wonder, how is it different from traditional programming? Well, in traditional programming, we would feed the input data and a well-written and tested program into a machine to generate output. When it comes to machine learning, input data, along with the output, is fed into the machine during the learning phase, and it works out a program for itself. To understand this better, refer to the illustration below:
History of Machine Learning
This section discusses the development of machine learning over the years. Today we are witnessing some astounding applications like self-driving cars, natural language processing and facial recognition systems making use of ML techniques for their processing. All this began in the year 1943, when Warren McCulloch a neurophysiologist along with a mathematician named Walter Pitts authored a paper that threw a light on neurons and its working. They created a model with electrical circuits and thus neural network was born.
The famous “Turing Test” was created in 1950 by Alan Turing, which would ascertain whether computers had real intelligence. It has to make a human believe that it is not a computer but a human instead, to get through the test. Arthur Samuel developed the first computer program that could learn as it played the game of checkers in the year 1952. The first neural network, called the perceptron was designed by Frank Rosenblatt in the year 1957.
The big shift happened in the 1990s when machine learning moved from being knowledge-driven to a data-driven technique due to the availability of huge volumes of data. IBM’s Deep Blue, developed in 1997 was the first machine to defeat the world champion in the game of chess. Businesses have recognized that the potential for complex calculations could be increased through machine learning. Some of the latest projects include: Google Brain, which was developed in 2012, was a deep neural network that focused on pattern recognition in images and videos. It was later employed to detect objects in YouTube videos. In 2014, Facebook created Deep Face, which can recognize people just like how humans do. In 2014, Deep Mind created a computer program called Alpha Go a board game that defeated a professional Go player. Due to its complexity, the game is said to be a very challenging yet classical game for artificial intelligence. Scientists Stephen Hawking and Stuart Russel have felt that if AI gains the power to redesign itself at an intensifying rate, then an unbeatable “intelligence explosion” may lead to human extinction. Musk characterizes AI as humanity’s “biggest existential threat.” Open AI is an organization created by Elon Musk in 2015 to develop safe and friendly AI that could benefit humanity. Recently, some of the breakthrough areas in AI are Computer Vision, Natural Language Processing and Reinforcement Learning.
Why Should We Learn Machine Learning?
Machine learning is a powerful tool that can be used to solve a wide range of problems. It allows computers to learn from data, without being explicitly programmed. This makes it possible to build systems that can automatically improve their performance over time by learning from their experiences.
There are many reasons why learning machine learning is important:
Machine learning is widely used in many industries, including healthcare, finance, and e-commerce. By learning machine learning, you can open up a wide range of career opportunities in these fields.
Machine learning can be used to build intelligent systems that can make decisions and predictions based on data. This can help organizations make better decisions, improve their operations, and create new products and services.
Machine learning is an important tool for data analysis and visualization. It allows you to extract insights and patterns from large datasets, which can be used to understand complex systems and make informed decisions.
Machine learning is a rapidly growing field with many exciting developments and research opportunities. By learning machine learning, you can stay up-to-date with the latest research and developments in the field.
Check out Machine Learning Course for Beginners to learn more.
How to get started with Machine Learning?
To get started, let’s take a look at some of the important terminologies.
Model: Also known as “hypothesis”, a machine learning model is the mathematical representation of a real-world process. A machine learning algorithm along with the training data builds a machine learning model.
Feature: A feature is a measurable property or parameter of the data-set.
Feature Vector: It is a set of multiple numeric features. We use it as an input to the machine learning model for training and prediction purposes.
Training: An algorithm takes a set of data known as “training data” as input. The learning algorithm finds patterns in the input data and trains the model for expected results (target). The output of the training process is the machine learning model.
Prediction: Once the machine learning model is ready, it can be fed with input data to provide a predicted output.
Target (Label): The value that the machine learning model has to predict is called the target or label.
Overfitting: When a massive amount of data trains a machine learning model, it tends to learn from the noise and inaccurate data entries. Here the model fails to characterize the data correctly.
Underfitting: It is the scenario when the model fails to decipher the underlying trend in the input data. It destroys the accuracy of the machine learning model. In simple terms, the model or the algorithm does not fit the data well enough.
There are Seven Steps of Machine Learning
Preparing that data
Choosing a model
It is mandatory to learn a programming language, preferably Python, along with the required analytical and mathematical knowledge. Here are the five mathematical areas that you need to brush up before jumping into solving Machine Learning problems:
Linear algebra for data analysis: Scalars, Vectors, Matrices, and Tensors
Mathematical Analysis: Derivatives and Gradients
Probability theory and statistics for Machine Learning
Algorithms and Complex Optimizations
How does Machine Learning work?
The three major building blocks of a system are the model, the parameters, and the learner.
Model is the system which makes predictions
The parameters are the factors which are considered by the model to make predictions
The learner makes the adjustments in the parameters and the model to align the predictions with the actual results
Let us build on the beer and wine example from above to understand how machine learning works. A machine learning model here has to predict if a drink is a beer or wine. The parameters selected are the color of the drink and the alcohol percentage. The first step is:
1. Learning from the training set
This involves taking a sample data set of several drinks for which the colour and alcohol percentage is specified. Now, we have to define the description of each classification, that is wine and beer, in terms of the value of parameters for each type. The model can use the description to decide if a new drink is a wine or beer.
You can represent the values of the parameters, ‘colour’ and ‘alcohol percentages’ as ‘x’ and ‘y’ respectively. Then (x,y) defines the parameters of each drink in the training data. This set of data is called a training set. These values, when plotted on a graph, present a hypothesis in the form of a line, a rectangle, or a polynomial that fits best to the desired results.
2. Measure error
Once the model is trained on a defined training set, it needs to be checked for discrepancies and errors. We use a fresh set of data to accomplish this task. The outcome of this test would be one of these four:
True Positive: When the model predicts the condition when it is present
True Negative: When the model does not predict a condition when it is absent
False Positive: When the model predicts a condition when it is absent
False Negative: When the model does not predict a condition when it is present
The sum of FP and FN is the total error in the model.
3. Manage Noise
For the sake of simplicity, we have considered only two parameters to approach a machine learning problem here that is the colour and alcohol percentage. But in reality, you will have to consider hundreds of parameters and a broad set of learning data to solve a machine learning problem.
The hypothesis then created will have a lot more errors because of the noise. Noise is the unwanted anomalies that disguise the underlying relationship in the data set and weakens the learning process. Various reasons for this noise to occur are:
Large training data set
Errors in input data
Data labelling errors
Unobservable attributes that might affect the classification but are not considered in the training set due to lack of data
You can accept a certain degree of training error due to noise to keep the hypothesis as simple as possible.
4. Testing and Generalization
While it is possible for an algorithm or hypothesis to fit well to a training set, it might fail when applied to another set of data outside of the training set. Therefore, It is essential to figure out if the algorithm is fit for new data. Testing it with a set of new data is the way to judge this. Also, generalisation refers to how well the model predicts outcomes for a new set of data.
When we fit a hypothesis algorithm for maximum possible simplicity, it might have less error for the training data, but might have more significant error while processing new data. We call this is underfitting. On the other hand, if the hypothesis is too complicated to accommodate the best fit to the training result, it might not generalise well. This is the case of over-fitting. In either case, the results are fed back to train the model further.
Which Language is Best for Machine Learning?
Python is famous for its readability and relatively lower complexity as compared to other programming languages. ML applications involve complex concepts like calculus and linear algebra which take a lot of effort and time to implement. Python helps in reducing this burden with quick implementation for the ML engineer to validate an idea. You can check out the Python Tutorial to get a basic understanding of the language. Another benefit of using Python is the pre-built libraries. There are different packages for a different type of applications, as mentioned below:
Numpy, OpenCV, and Scikit are used when working with images
NLTK along with Numpy and Scikit again when working with text
Librosa for audio applications
Matplotlib, Seaborn, and Scikit for data representation
TensorFlow and Pytorch for Deep Learning applications
Scipy for Scientific Computing
Django for integrating web applications
Pandas for high-level data structures and analysis
Here is a summary:
Difference Between Machine Learning, Artificial Intelligence and Deep Learning
The field of computer science aims to create intelligent machines that can think and function like humans.
A subfield of artificial intelligence that focuses on developing algorithms and models that can learn from data rather than being explicitly programmed.
A subfield of machine learning that uses multi-layered artificial neural networks to learn complex patterns in data.
Here is a brief summary of the main differences between these concepts:
Artificial intelligence is a broad field that encompasses a variety of techniques and approaches for creating intelligent systems.
The practice of teaching algorithms to learn from data rather than being explicitly programmed is known as machine learning, which is a subset of artificial intelligence.
Deep learning is a branch of machine learning that use multiple layers of artificial neural networks to discover intricate data patterns.
Introduction to Artificial Intelligence Machine learning Course for Beginners
Types of Machine Learning
There are three main types:
Supervised learning is a class of problems that uses a model to learn the mapping between the input and target variables. Applications consisting of the training data describing the various input variables andthe target variable are known as supervised learning tasks.
Let the set of input variable be (x) and the target variable be (y). A supervised learning algorithm tries to learn a hypothetical function which is a mapping given by the expression y=f(x), which is a function of x.
The learning process here is monitored or supervised. Since we already know the output the algorithm is corrected each time it makes a prediction, to optimize the results. Models are fit on training data which consists of both the input and the output variable and then it is used to make predictions on test data. Only the inputs are provided during the test phase and the outputs produced by the model are compared with the kept back target variables and is used to estimate the performance of the model.
There are basically two types of supervised problems: Classification – which involves prediction of a class label and Regression – that involves the prediction of a numerical value.
The MINST handwritten digits data set can be seen as an example of classification task. The inputs are the images of handwritten digits, and the output is a class label which identifies the digits in the range 0 to 9 into different classes.
The Boston house price data set could be seen as an example of Regression problem where the inputs are the features of the house, and the output is the price of a house in dollars, which is a numerical value.
In an unsupervised learning problem the model tries to learn by itself and recognize patterns and extract the relationships among the data. As in case of a supervised learning there is no supervisor or a teacher to drive the model. Unsupervised learning operates only on the input variables. There are no target variables to guide the learning process. The goal here is to interpret the underlying patterns in the data in order to obtain more proficiency over the underlying data.
There are two main categories in unsupervised learning; they are clustering – where the task is to find out the different groups in the data. And the next is Density Estimation – which tries to consolidate the distribution of data. These operations are performed to understand the patterns in the data. Visualization and Projection may also be considered as unsupervised as they try to provide more insight into the data. Visualization involves creating plots and graphs on the data and Projection is involved with the dimensionality reduction of the data.
Reinforcement learning is type a of problem where there is an agent and the agent is operating in an environment based on the feedback or reward given to the agent by the environment in which it is operating. The rewards could be either positive or negative. The agent then proceeds in the environment based on the rewards gained.
The reinforcement agent determines the steps to perform a particular task. There is no fixed training dataset here and the machine learns on its own.
Playing a game is a classic example of a reinforcement problem, where the agent’s goal is to acquire a high score. It makes the successive moves in the game based on the feedback given by the environment which may be in terms of rewards or a penalization. Reinforcement learning has shown tremendous results in Google’s AplhaGo of Google which defeated the world’s number one Go player.
Advantages and Disadvantages
Everything comes with a few advantages and disadvantages. In this section, let’s talk about a few of the basic advantages and disadvantages of ML.
It can be used for pattern detection.
It can be used to make predictions about future data.
It can be used to generate new features from data automatically.
It can be used to cluster data automatically.
It can be used to detect outliers in data automatically.
Some disadvantages include the potential for biased data, overfitting data, and lack of explainability.
Machine Learning Algorithms
There are a variety of machine learning algorithms available and it is very difficult and time consuming to select the most appropriate one for the problem at hand. These algorithms can be grouped in to two categories. Firstly, they can be grouped based on their learning pattern and secondly by their similarity in their function.
Based on their learning style they can be divided into three types:
Supervised Learning Algorithms: The training data is provided along with the label which guides the training process. The model is trained until the desired level of accuracy is attained with the training data. Examples of such problems are classification and regression. Examples of algorithms used include Logistic Regression, Nearest Neighbor, Naive Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM), Neural Networks.
Unsupervised Learning Algorithms: Input data is not labeled and does not come with a label. The model is prepared by identifying the patterns present in the input data. Examples of such problems include clustering, dimensionality reduction and association rule learning. List of algorithms used for these type of problems include Apriori algorithm and K-Means and Association Rules
Semi-Supervised Learning Algorithms: The cost to label the data is quite expensive as it requires the knowledge of skilled human experts. The input data is combination of both labeled and unlabelled data. The model makes the predictions by learning the underlying patterns on their own. It is a mix of both classification and clustering problems.
Based on the similarity of function, the algorithms can be grouped into the following:
Regression Algorithms: Regression is a process that is concerned with identifying the relationship between the target output variables and the input features to make predictions about the new data. Top six Regression algorithms are: Simple Linear Regression, Lasso Regression, Logistic regression, Multivariate Regression algorithm, Multiple Regression Algorithm.
Instance-based Algorithms: These belong to the family of learning that measures new instances of the problem with those in the training data to find out a best match and makes a prediction accordingly. The top instance-based algorithms are: k-Nearest Neighbor, Learning Vector Quantization, Self-Organizing Map, Locally Weighted Learning, and Support Vector Machines.
Regularization: Regularization refers to the technique of regularizing the learning process from a particular set of features. It normalizes and moderates. The weights attached to the features are normalized, which prevents in certain features from dominating the prediction process. This technique helps to prevent the problem of overfitting in machine learning. The various regularization algorithms are Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO) and Least-Angle Regression (LARS).
Decision Tree Algorithms: These methods construct a tree-based model constructed on the decisions made by examining the values of the attributes. Decision trees are used for both classification and regression problems. Some of the well-known decision tree algorithms are: Classification and Regression Tree, C4.5 and C5.0, Conditional Decision Trees, Chi-squared Automatic Interaction Detection and Decision Stump.
Bayesian Algorithms: These algorithms apply the Bayes theorem for classification and regression problems. They include Naive Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes, Bayesian Belief Network, Bayesian Network and Averaged One-Dependence Estimators.
Clustering Algorithms: Clustering algorithms involve the grouping of data points into clusters. All the data points that are in the same group share similar properties and, data points in different groups have highly dissimilar properties. Clustering is an unsupervised learning approach and is mostly used for statistical data analysis in many fields. Algorithms like k-Means, k-Medians, Expectation Maximisation, Hierarchical Clustering, and Density-Based Spatial Clustering of Applications with Noise fall under this category.
Association Rule Learning Algorithms: Association rule learning is a rule-based learning method for identifying the relationships between variables in a very large dataset. Association Rule learning is employed predominantly in market basket analysis. The most popular algorithms are: Apriori algorithm and Eclat algorithm.
Artificial Neural Network Algorithms: Artificial neural network algorithms relies find its base from the biological neurons in the human brain. They belong to the class of complex pattern matching and prediction processes in classification and regression problems. Some of the popular artificial neural network algorithms are: Perceptron, Multilayer Perceptrons, Stochastic Gradient Descent, Back-Propagation, , Hopfield Network, and Radial Basis Function Network.
Deep Learning Algorithms: These are modernized versions of artificial neural network, that can handle very large and complex databases of labeled data. Deep learning algorithms are tailored to handle text, image, audio and video data. Deep learning uses self-taught learning constructs with many hidden layers, to handle big data and provides more powerful computational resources. The most popular deep learning algorithms are: Some of the popular deep learning ms include Convolutional Neural Network, Recurrent Neural Networks, Deep Boltzmann Machine, Auto-Encoders Deep Belief Networks and Long Short-Term Memory Networks.
Dimensionality Reduction Algorithms: Dimensionality Reduction algorithms exploit the intrinsic structure of data in an unsupervised manner to express data using reduced information set. They convert a high dimensional data into a lower dimension which could be used in supervised learning methods like classification and regression. Some of the well known dimensionality reduction algorithms include Principal Component Analysis, Principal Component Regressio, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Mixture Discriminant Analysis, Flexible Discriminant Analysis and Sammon Mapping.
Ensemble Algorithms: Ensemble methods are models made up of various weaker models that are trained separately and the individual predictions of the models are combined using some method to get the final overall prediction. The quality of the output depends on the method chosen to combine the individual results. Some of the popular methods are: Random Forest, Boosting, Bootstrapped Aggregation, AdaBoost, Stacked Generalization, Gradient Boosting Machines, Gradient Boosted Regression Trees and Weighted Average.
Applications of Machine Learning
These algorithms help in building intelligent systems that can learn from their past experiences and historical data to give accurate results. Many industries are thus applying ML solutions to their business problems, or to create new and better products and services. Healthcare, defense, financial services, marketing, and security services, among others, make use of ML.
1. Facial recognition/Image recognition
The most common application is Facial Recognition, and the simplest example of this application is the iPhone. There are a lot of use-cases of facial recognition, mostly for security purposes like identifying criminals, searching for missing individuals, aid forensic investigations, etc. Intelligent marketing, diagnose diseases, track attendance in schools, are some other uses.
2. Automatic Speech Recognition
Abbreviated as ASR, automatic speech recognition is used to convert speech into digital text. Its applications lie in authenticating users based on their voice and performing tasks based on the human voice inputs. Speech patterns and vocabulary are fed into the system to train the model. Presently ASR systems find a wide variety of applications in the following domains:
Forensic and Law enforcement
Defense & Aviation
Home Automation and Security Access Control
I.T. and Consumer Electronics
3. Financial Services
Machine learning has many use cases in Financial Services. Machine Learning algorithms prove to be excellent at detecting frauds by monitoring activities of each user and assess that if an attempted activity is typical of that user or not. Financial monitoring to detect money laundering activities is also a critical security use case.
It also helps in making better trading decisions with the help of algorithms that can analyze thousands of data sources simultaneously. Credit scoring and underwriting are some of the other applications. The most common application in our day to day activities is the virtual personal assistants like Siri and Alexa.
4. Marketing and Sales
It is improving lead scoring algorithms by including various parameters such as website visits, emails opened, downloads, and clicks to score each lead. It also helps businesses to improve their dynamic pricing models by using regression techniques to make predictions.
Sentiment Analysis is another essential application to gauge consumer response to a specific product or a marketing initiative. Machine Learning for Computer Vision helps brands identify their products in images and videos online. These brands also use computer vision to measure the mentions that miss out on any relevant text. Chatbots are also becoming more responsive and intelligent.
A vital application is in the diagnosis of diseases and ailments, which are otherwise difficult to diagnose. Radiotherapy is also becoming better.
Early-stage drug discovery is another crucial application which involves technologies such as precision medicine and next-generation sequencing. Clinical trials cost a lot of time and money to complete and deliver results. Applying ML based predictive analytics could improve on these factors and give better results.
These technologies are also critical to make outbreak predictions. Scientists around the world are using ML technologies to predict epidemic outbreaks.
6. Recommendation Systems
Many businesses today use recommendation systems to effectively communicate with the users on their site. It can recommend relevant products, movies, web-series, songs, and much more. Most prominent use-cases of recommendation systems are e-commerce sites like Amazon, Flipkart, and many others, along with Spotify, Netflix, and other web-streaming channels.
Real-world machine learning use cases
Fraud detection: Machine learning algorithms can be trained to detect patterns of fraudulent behavior, such as suspicious transactions or fake accounts.
Image and speech recognition: Machine learning algorithms can be used to recognize and classify objects, people, and spoken words in images and audio recordings.
Predictive maintenance: Equipment maintenance can be planned ahead of time to save downtime using machine learning to predict when it is likely to fail.
Personalization: Machine learning can be used to personalize recommendations and advertisements, such as those seen on online shopping websites or streaming services.
Healthcare: Machine learning can be used to predict patient outcomes, identify potential outbreaks of infectious diseases, and assist with diagnosis and treatment planning.
Natural language processing: Machine learning can be used to understand and process human language, enabling applications such as language translation and chatbots.
Future of Machine Learning
Given that machine learning is a constantly developing field that is influenced by numerous factors, it is challenging to forecast its precise future. Machine learning, however, is most likely to continue to be a major force in many fields of science, technology, and society as well as a major contributor to technological advancement. The creation of intelligent assistants, personalized healthcare, and self-driving automobiles are some potential future uses for machine learning. Important global issues like poverty and climate change may be addressed via machine learning.
It is also likely that machine learning will continue to advance and improve, with researchers developing new algorithms and techniques to make machine learning more powerful and effective. One area of active research in this field is the development of artificial general intelligence (AGI), which refers to the development of systems that have the ability to learn and perform a wide range of tasks at a human-like level of intelligence.
1. What exactly is machine learning?
Arthur Samuel coined the term Machine Learning in 1959. He defined it as “The field of study that gives computers the capability to learn without being explicitly programmed”. It is a subset of Artificial Intelligence and it allows machines to learn from their experiences without any coding.
2. What is machine learning used for?
Machine Learning is used in our daily lives much more than we know it. These are areas where it is used:
Online Fraud Detection
Email Spam Filtering
3. What is difference between machine learning and artificial intelligence?
A technology that enables a machine to stimulate human behavior to help in solving complex problems is known as Artificial Intelligence. Machine Learning is a subset of AI and allows machines to learn from past data and provide an accurate output. AI deals with unstructured as well as structured data. Whereas, Machine Learning deals with structured and semi-structured data.
4. How Machine Learning works?
The typical machine learning process involves three steps: Training, Validation, and Testing. The first step is to learn from the training set provided, the second step is to measure error, the third step involves managing noise and testing all the parameters. These are the basic steps followed and a very broad description on how it works.
5. What are the types of Machine Learning?
The broad types of machine learning are:
Supervised Machine Learning
Unsupervised Machine Learning
6. What is the best language for machine learning?
7. Is Alexa a machine learning?
Alexa is a virtual assistant that is created by Amazon and is also known as Amazon Alexa. This virtual assistant was created using machine learning and artificial intelligence technologies.
8. Is Siri a machine learning?
Similar to Alexa, Siri is also a virtual or a personal assistant. Siri was created by Apple and makes use of voice technology to perform certain actions. Siri also makes use of machine learning and deep learning to function.
9. Why is machine learning popular?
The amount of data available to us is constantly increasing. Machines make use of this data to learn and improve the results and outcomes provided to us. These outcomes can be extremely helpful in providing valuable insights and taking informed business decisions as well. It is constantly growing, and with that, the applications are growing as well. We make use of machine learning in our day-to-day life more than we know it. In the future, it is only said to grow further and help us. Thus, it is popular.