Tutorial Playlist

Machine Learning Tutorial: A Step-by-Step Guide for Beginners

Overview

An Introduction To Machine Learning

Lesson - 1

What is Machine Learning and How Does It Work?

Lesson - 2

The Complete Guide to Understanding Machine Learning Steps

Lesson - 3

Top 10 Machine Learning Applications in 2020

Lesson - 4

An Introduction to the Types Of Machine Learning

Lesson - 5

Supervised and Unsupervised Learning in Machine Learning

Lesson - 6

Everything You Need to Know About Feature Selection

Lesson - 7

Linear Regression in Python

Lesson - 8

Everything You Need to Know About Classification in Machine Learning

Lesson - 9

An Introduction to Logistic Regression in Python

Lesson - 10

Understanding the Difference Between Linear vs. Logistic Regression

Lesson - 11

The Best Guide On How To Implement Decision Tree In Python

Lesson - 12

Random Forest Algorithm

Lesson - 13

Understanding Naive Bayes Classifier

Lesson - 14

The Best Guide to Confusion Matrix

Lesson - 15

How to Leverage KNN Algorithm in Machine Learning?

Lesson - 16

K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases

Lesson - 17

PCA in Machine Learning - Your Complete Guide to Principal Component Analysis

Lesson - 18

What is Cost Function in Machine Learning

Lesson - 19

The Ultimate Guide to Cross-Validation in Machine Learning

Lesson - 20

An Easy Guide to Stock Price Prediction Using Machine Learning

Lesson - 21

What Is Reinforcement Learning? The Best Guide To Reinforcement Learning

Lesson - 22

What Is Q-Learning? The Best Guide to Understand Q-Learning

Lesson - 23

The Best Guide to Regularization in Machine Learning

Lesson - 24

Everything You Need to Know About Bias and Variance

Lesson - 25

The Complete Guide on Overfitting and Underfitting in Machine Learning

Lesson - 26

Mathematics for Machine Learning - Important Skills You Must Possess

Lesson - 27

A One-Stop Guide to Statistics for Machine Learning

Lesson - 28

Embarking on a Machine Learning Career? Here’s All You Need to Know

Lesson - 29

How to Become a Machine Learning Engineer?

Lesson - 30

Top 34 Machine Learning Interview Questions and Answers in 2021

Lesson - 31
An Introduction to the Types Of Machine Learning

Machine Learning has found its applications in almost every business sector. There are several algorithms used in machine learning that help you build complex models. Each of these algorithms in machine learning can be classified into a certain category. In this article, we’ll learn about the types of machine learning. This will give you better insight into the field of machine learning.

Stated below are the topics we will explore in this article on the Types of Machine Learning:

  • What is Machine Learning? 
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that enables systems to learn from vast volumes of data and solve specific problems. It uses computer algorithms that improve their efficiency automatically through experience.

WhatIsMachineLearning

There are primarily three types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.

TypesOfMachineLearning

Let’s explore and understand the different types of machine learning one by one.

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Supervised Learning

Overview:

Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs. 

An example of supervised learning is to train a system that identifies the image of an animal. 

Attached below, you can see that we have our trained model that identifies the picture of a cat.

SupervisedLearning

Algorithms:

Some of the most popularly used supervised learning algorithms are:

  • Linear Regression 
  • Logistic Regression 
  • Support Vector Machine
  • K Nearest Neighbor 
  • Decision Tree
  • Random Forest
  • Naive Bayes

Working:

Supervised learning algorithms take labeled inputs and map them to the known outputs, which means you already know the target variable.

Now, let’s focus on the training process for the supervised learning method.

Supervised Learning methods need external supervision to train machine learning models. Hence, the name supervised. They need guidance and additional information to return the desired result.

Applications:

Supervised learning algorithms are generally used for solving classification and regression problems. 

SupervisedLearningAlgorithms

Few of the top supervised learning applications are weather prediction, sales forecasting, stock price analysis.

SupervisedLearningApplications

Now that you understand what Supervised learning is, let’s see the next type of machine learning.

Unsupervised Learning

Overview:

Unsupervised learning is a type of machine learning that uses unlabeled data to train machines. Unlabeled data doesn’t have a fixed output variable. The model learns from the data, discovers the patterns and features in the data, and returns the output. 

Depicted below is an example of an unsupervised learning technique that uses the images of vehicles to classify if it’s a bus or a truck. The model learns by identifying the parts of a vehicle, such as a length and width of the vehicle, the front, and rear end covers, roof hoods, the types of wheels used, etc. Based on these features, the model classifies if the vehicle is a bus or a truck.

UnsupervisedLearning

Algorithms:

Selecting the right algorithm depends on the type of problem you are trying to solve. Some of the common examples of unsupervised learning are:

  • K Means Clusterin
  • Hierarchical Clustering 
  • DBSCAN 
  • Principal Component Analysis

Working:

Unsupervised learning finds patterns and understands the trends in the data to discover the output. So, the model tries to label the data based on the features of the input data.

The training process used in unsupervised learning techniques does not need any supervision to build models. They learn on their own and predict the output.

Applications:

Unsupervised learning is used for solving clustering and association problems.

UnsupervisedLearningAlgorithms

One of the applications of unsupervised learning is customer segmentation. Based on customer behavior, likes, dislikes, and interests, you can segment and cluster similar customers into a group. Another example where unsupervised learning algorithms are used is used churn rate analysis.

UnsupervisedLearningApplications

Let’s see the third type of machine learning, i.e., reinforcement learning.

Reinforcement Learning

Overview

Reinforcement Learning trains a machine to take suitable actions and maximize its rewards in a particular situation. It uses an agent and an environment to produce actions and rewards. The agent has a start and an end state. But, there might be different paths for reaching the end state, like a maze. In this learning technique, there is no predefined target variable. 

An example of reinforcement learning is to train a machine that can identify the shape of an object, given a list of different objects. In the example shown, the model tries to predict the shape of the object, which is a square in this case.

ReinforcementLearning

Algorithms

Some of the important reinforcement learning algorithms are:

  1. Q-learning 
  2. Sarsa 
  3. Monte Carlo 
  4. Deep Q network

Working

Reinforcement learning follows trial and error methods to get the desired result. After accomplishing a task, the agent receives an award. An example could be to train a dog to catch the ball. If the dog learns to catch a ball, you give it a reward, such as a biscuit.

Reinforcement Learning methods do not need any external supervision to train models.

Reinforcement learning problems are reward-based. For every task or for every step completed, there will be a reward received by the agent. If the task is not achieved correctly, there will be some penalty added. 

RewaredBasedApproach

Now, let’s see what applications we have in reinforcement learning.

Applications

Reinforcement learning algorithms are widely used in the gaming industries to build games. It is also used to train robots to do human tasks.

ReinforcementLearningApplications

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Conclusion

After reading this article, you would have learned the basics of machine learning and its different types. You would have understood the training process and the types of problems they solve. Finally, you looked at the various applications of machine learning.

Do you have any questions related to this article on types of machine learning? If you have, then please put them in the comments section. Our team will help you solve your queries.

To start your career in machine learning, click on the following link: Machine Learning.

About the Author

Kartik MenonKartik Menon

Kartik is an experienced content strategist and an accomplished technology marketing specialist passionate about designing engaging user experiences with integrated marketing and communication solutions.

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