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Copyright 2020 by AI Publishing

All rights reserved.

First Printing, 2020

Edited by AI Publishing

eBook Converted and Cover by Gazler Studio

Published by AI Publishing LLC

ISBN-13: 978-1-7347901-5-3

The contents of this book may not be copied, reproduced, duplicated, or transmitted without the direct written permission of the author. Under no circumstances whatsoever will any legal liability or blame be held against the publisher for any compensation, damages, or monetary loss due to the information contained herein, either directly or indirectly.

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Table of Contents

Preface

Thank you for your decision on purchasing this book. I can assure you that you will not regret your decision. The saying data is the new oil is no longer a mere cliche. Data is actually powering the industries of today. Organizations and companies need to improve their growth, which depends on correct decision making. Accurate decision making requires facts and figures and statistical analysis of data. Data science does exactly that. With data and machine learning, you can extract and visualize data in detail and create statistical models, which, in turn, help you in decision making. In this book, you will learn all these concepts. So, buckle up for a journey that may give you your career break!

Book Approach

The book follows a very simple approach. It is divided into 10 chapters. The first five chapters of the book are dedicated to data analysis and visualization, while the last five chapters are based on machine learning and statistical models for data science. Chapter 1 provides a very brief introduction to data science and machine learning and provides a roadmap for step by step learning approach to data science and machine learning. The process for environment setup, including the software needed to run scripts in this book, is also explained in this chapter.

Chapter 2 contains a crash course on Python for beginners. If you are already familiar with Python, you can skip Chapter 2. Chapter 3 and chapter 4 explain the use of NumPy and Pandas libraries, respectively, for data analysis. Chapter 5 explains the process of data visualization using Pythons data visualization libraries such as Matplotlib, Seaborn, and Pandas.

Chapters 6 and 7 provide an introduction to supervised machine learning approaches like regression and classification with the help of the Scikit Learn library. Chapter 8 explains unsupervised machine learning, where you study different clustering approaches for machine learning. Chapter 9 details the introduction to deep learning with TensorFlow 2.0 library, where you will study densely connected neural networks, recurrent neural networks, and convolutional neural networks. Finally, dimensionality reduction approaches have been discussed in the 10 th chapter of this book.

In each chapter, an explanation of theoretical concepts is followed by practical examples. Each chapter also contains exercises that students can use to evaluate their understanding of the concepts explained in the chapter. The Python notebook for each chapter is provided in the Source Codes folder in the GitHub repository. It is advised that instead of copying the code, you write the code yourself, and in case of an error, you match your code with the corresponding Python notebook, find and then correct the error. You can download the datasets used in this book either at runtime or in the Datasets folder in the GitHub repository.

Who Is This Book For?

This book explains different data science and machine learning concepts with the help of examples using various Python libraries. The book is aimed ideally at absolute beginners to data science and machine learning. Though a background in the Python programming language and feature engineering can help speed up learning, the book contains a crash course on Python programming language in the first chapter. Therefore, the only prerequisites to efficiently using this book are access to a computer with internet and basic knowledge of linear algebra and calculus. All the codes and datasets have been provided. However, to download data preparation libraries, you will need the internet.

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