Machine Learning

Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition)

Dr Ruchi Doshi, , Ritesh Kumar Jain,

• Publisher: BPB Publications
• Year: 2021
• ISBN:
  9789391392352
• Topic: Ciencia de la computación
• Subtopic: Tecnología de la información

Concepts of Machine Learning with Practical Approaches.

Key Features
? Includes real-scenario examples to explain the working of Machine Learning algorithms.
? Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks.
? Full of Python codes, numerous exercises, and model question papers for data science students.

Description
The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches.This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning.By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems.

What you will learn
? Perform feature extraction and feature selection techniques.
? Learn to select the best Machine Learning algorithm for a given problem.
? Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib.
? Practice how to implement different types of Machine Learning techniques.

Who this book is for
This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cases. Knowing basic statistical and programming concepts would be good, although not mandatory.

Table of Contents
1. Introduction
2. Supervised Learning Algorithms
3. Unsupervised Learning
4. Introduction to the Statistical Learning Theory
5. Semi-Supervised Learning and Reinforcement Learning
6. Recommended Systems

About the Authors
Dr Ruchi Doshi has more than 14 years of academic, research, and software development experience in Asia and Africa. Currently, she is working as a research supervisor at the Azteca University, Mexico, and as an adjunct faculty at the Jyoti Vidyapeeth Women's University, Jaipur, Rajasthan, India. She has also worked with the BlueCrest University College, Liberia, West Africa as a Registrar and Head, Examination; BlueCrest University College, Ghana, Africa; Amity University, Rajasthan, India; Trimax IT Infrastructure & Services, Udaipur, India. Kamal Kant Hiran works as an Assistant Professor, School of Engineering at the Sir Padampat Singhania University (SPSU), Udaipur, Rajasthan, India as well as a Research Fellow at the Aalborg University, Copenhagen, Denmark. He is a Gold Medalist in M.Tech. (Hons.). He has more than 16 years of experience as an academic and researcher in Asia, Africa, and Europe. Ritesh Kumar Jain works as an Assistant Professor, at the Geetanjali Institute of Technical Studies, (GITS), Udaipur, Rajasthan, India. He has more than 15 years of teaching and research experience. Dr. Kamlesh Lakhwani works as an Associate Professor, in Computer Science & Engineering at JECRC University Jaipur, Rajasthan, India. He has an excellent academic background and a rich experience of 15 years as an academician and researcher in Asia.

CHAPTER 1

Introduction to Machine Learning

“Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed”.

~ Arthur l. Samuel, Computer scientist

Introduction

Machine learning is an application of Artificial Intelligence. While AI is the umbrella term given to machines emulating human abilities, machine learning is a specific branch of AI where machines are trained to learn how to process and make use of data. The objective of machine learning is not only effective data collection but also to make use of the ever-increasing amounts of data being gathered by manipulating and analyzing them without heavy human input.

This chapter takes on a proactive and practical approach to discuss the foundation, background concepts, characteristics as well as the pros and cons of Machine Learning. We will discuss from traditional programming practices to Machine Learning. This chapter includes the types of Machine Learning as well as their advantages & disadvantage.

Structure

In this chapter, we will discuss the following topics:

• What is Machine Learning?
• Machine Learning Vs. Traditional Programming
• The Seven Steps of Machine Learning
• Types of Machine Learning
• Advantages and disadvantages of Machine Learning
• Popular Machine Learning Software Tools
• Tools used for practical with some examples

Objectives

By completing this chapter, you will be able to:

• Understand the concepts of Machine Learning.
• Understand the difference Between Traditional programming & Machine Learning.
• Understand about the state-of-art applications of Machine Learning.
• Understand about the types of Machine Learning.
• Understand the significance of the Machine Learning applications
• Understand about the tools of Machine Learning

What is Machine Learning?

Machine learning is a sub-domain of artificial intelligence (AI). The goal of machine learning is usually to understand the structure of the data and to match that data to models that can be understood and used by humans.

While artificial intelligence and machine learning are often used together, they are two different concepts. AI is a broad concept – decision-making machines, learning new skills, and problem-solving in the same way for people - and machine learning is an AI set that enables intelligent systems to independently learn new things from the data.

Figure 1.1: Machine Learning subset

The Figure 1.1 shows that machine learning is a subset of artificial intelligence. Machine learning is a tool for transforming information into knowledge. In the previous 50 years, there has been a blast of information/data. This mass of information is pointless except if we investigate it and discover the examples covered up inside. Machine learning techniques are utilized to consequently locate the significant fundamental examples inside complex information that we would somehow battle to find. Hidden patterns and information about the problem can be used to predict future events and to make all sorts of complex decisions.

We have seen machine learning as a trendy expression for hardly any years, the meaning behind this could be the high rate of data/information creation by applications, the expansion of computation power over the years, and the development of better algorithms.

Figure 1.2: Human & Robot

The Figure 1.2, shows that the human learns everything automatically from experience & the robot can also learn from previous experience data with the help of machine learning.

The name machine learning was coined by Arthur Samuel in 1959, an American pioneer in the field of computer gaming and artificial intelligence who stated that “Machine Learning gives computers the ability to learn without being explicitly programmed”. Arthur Samuel created the first self-study program for playing checkers. You realize that the more the system plays, the better it performs.

And in 1997, Tom Mitchell gave a “well-established” mathematical and relational definition that “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.

Example 1: Playing Chess

• Task (T): The task of playing chess
• Experience (E): The experience of playing many games of chess
• Performance Measure (P): The probability of the program which will win the next game of chess

Example 2: Spam Mail Detection

• Task (T): To recognize and classify the emails into 'spam' or 'not spam'.
• Experience (E): A set of emails with given labels ('spam' / 'not spam').
• Performance Measure (P): Total percentage of emails being correctly classified as 'spam' (or 'not spam’) by the program.

The basic premise of machine learning is to build algorithms that can obtain input data and use mathematical analysis to predict output while reviewing results as new data becomes available.

Currently, machine learning (ML) is used for a variety of tasks such as image recognition, speech recognition like Amazon’s Alexa, email filtering like email is spam or not spam, Facebook auto-tagging, recommendation systems like Amazon and Flipkart recommends the product to the user and many more.

Machine Learning versus Traditional Programming

Traditional programming is a process of hand - meaning that the programmer makes the program. But apart from anyone programming the logic, one has to manually decide the rules or code. We have input data, and the programmer wrote the program/rules that use that data and execute on a computer to generate the output/ answer as shown in the following Figure 1.3:

Figure 1.3: Traditional Programming

On the other hand, in case of Machine Learning, data and output/answers (or labels) come in as input and the learning rules (models) come out as output as shown in Figure 1.4. The machine learning paradigm is especially important because it allows the computer to learn new rules in a complex and advanced environment, a space difficult to understand by humans.

Figure 1.4: Machine Learning

For example, we could write a traditional computer program for activity recognition (walking, running, or cycling) based on a person's speed (data) and an activity description (walk, run, and cycling) based on speed (rules). However, the problem with this method is that different people walk, run, and ride bikes at different speeds depending on age, environment, health, and so on.

If we have to solve the same problem in the field of machine learning, we can find many examples of tasks and their labels (answers i.e., type of activity) and learn or infer the rules for predicting future work.

The Seven Steps of Machine Learning

The process of machine learning can be broken down into 7 steps as shown in Figure 1.5. To illustrate the significance and function of each step, we would be using an example of a simple model. This model would be responsible for differentiating between an apple and an orange. Machine learning is capable of much for complex tasks. However, to explain the process in simplistic terms, a basic example is taken to explain the relevant concepts:

Figure 1.5: Steps of Machine Learning

Step 1: Data Gathering / Data Collection

This step is very important because the quality and quantity of data th...