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Author：Gaurav Leekha
Build AI applications using Python to intelligently interact with the world around you. KEY FEATURES ● Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. ● Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. ● Covers deep learning models such as R-CNN and YOLO for object recognition and teaches how to build an image classifier using CNN. DESCRIPTION The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems. WHAT YOU WILL LEARN ● Learn to implement various machine learning and deep learning algorithms to achieve smart results. ● Understand how ML algorithms can be applied to real-life applications. ● Explore logic programming and learn how to use it practically to solve real-life problems. ● Learn to develop different types of artificial neural networks with Python. ● Understand reinforcement learning and how to build an environment and agents using Python. ● Work with NLT
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Learn AI with Python Explore Machine Learning and Deep Learning Techniques for Building Smart AI Systems Using Scikit-Learn, NLTK, NeuroLab, and Keras Gaurav Leekha www.bpbonline.com
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FIRST EDITION 2022 Copyright © BPB Publications, India ISBN: 978-93-91392-611 All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The information contained in this book is true to correct and the best of author’s and publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information.
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www.bpbonline.com
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Dedicated to Aarav Leekha My son, the beat of my heart, and the energy of my soul.
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About the Author Gaurav Leekha is a Deep Learning researcher. He has 7 years of academic experience of teaching technical courses along with 5+ years of technical content creation as a freelancer on variety of topics related to Machine Learning, Deep Learning, Artificial Intelligence, and Web Development Technologies. He has authored a few research papers published in renowned journals. He is also the reviewer of prominent journals and has been the technical reviewer for various online courses. He has also earned multiple certifications in the field of machine learning and deep learning. Outside work, Gaurav likes to cook food for his family, play with his eight-year-old son and practice vipassana meditation.
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About the Reviewer Bharat Sikka is the author of the book “Elements of Deep Learning for Computer Vision” and a Data Scientist based in Mumbai, India. Over the years, he has worked on implementing algorithms in Artificial Intelligence in domains like Financial Risk, Fraud, and Governance, Computer Vision among others and is currently working as a Data Scientist at State Bank of India. He also has a thorough knowledge and understanding of various programming languages such as Python, R, MATLAB and Octave for Machine Learning, Deep Learning, Data Visualization and Analysis in Python, R and through Power BI, Tableau. Bharat holds a MS in Data Science and Analytics from Royal Holloway, University of London and BTech in Information Technology from Symbiosis International University and has earned multiple certifications including MOOCs on varied fields including machine learning. He is a science fiction fanatic, loves travelling and a great cook.
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Acknowledgement First, I would like to express my gratitude to God whose blessings inspired me to write this book. I strongly believe in sharing my knowledge and helping others to succeed. This book wouldn’t have happened if I hadn’t had the support of my caring parents, my loving wife, and my genius son. I will take this opportunity to thank them for their continued support. My sincere thanks to my elder brother and sister for encouraging and believing in me always. Words are not enough to express my gratitude to Dr. Rajesh Kumar Aggarwal, Professor, Department of Computer Engineering, NIT-Kurukshetra, for his continued guidance and insightful comments. Along with that, accept my heartful gratitude for your time and support to motivate me and other people towards the path of spirituality and humanity. Finally, I would also thank my friends who trust my abilities and knowledge to write this book.
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Preface Artificial Intelligence has existed for a long time and proven to be a disruptive force in the modern world where everything is driven by data and automation. From newspapers to TV channels, the hype around AI these days is ubiquitous and due to a huge improvement in the field of AI, it along with its subfields-Machine Learning and Deep Learning-has become a buzzword in recent years. AI is used extensively across many fields, such as robotics, object detection, image recognition, speech recognition, self-driving vehicles, humanoid robots, recommender system, chatbots, Virtual personal assistants, and so on. The primary goal of this book is to let you explore some real-world scenarios and understand where and which algorithms to use in each context. This exciting recipe- based book also contain functional codes written in Python. Over the 10 chapters in this book, you will learn the following: Chapter 1 covers the basics of Artificial Intelligence and explains all the important terms and definitions. It also explains various fields of study in AI and applications of AI in various industries. It will assist you in installing the Python programming language on different platforms. Chapter 2 covers the basics of Machine Learning and its different learning styles. It also introduces you to the most popular
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machine learning algorithms and their implementation using Python. Chapter 3 deals with supervised machine learning tasks namely Classification and Regression. It covers various steps to build a classifier and regressor using Python. It also discusses various performance metrics used to evaluate classification and regression models. Chapter 4 deals with unsupervised machine learning tasks namely Clustering. It covers some important ML clustering algorithms and their implementation using Python. It also discusses various metrics used to evaluate the performance of clustering algorithms. Chapter 5 covers logic programming with some implementation examples useful for solving problems in the real-life domain. Chapter 6 discusses, in-depth, what is Natural Language Processing (NLP) and how to implement it in Python. It introduces you to Python’s Natural Language Toolkit (NLTK). It then shows how you can implement various important concepts of NLP using NLTK. Chapter 7 describes the working of an automatic speech recognition (ASR) system. It also covers various steps to build a classifier and regressor using Python. Chapter 8 discusses Artificial Neural Network (ANN) in detail. It then covers building some useful neural networks such as Single
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layer neural networks, Multilayer neural networks, etc., in Python. Chapter 9 is a key chapter that discusses, in detail, reinforcement learning and its building blocks namely agent and environment. It describes how to construct an environment and agent using the Python programming language. Chapter 10 is another key chapter, covering the basics of deep learning and convolutional neural networks (CNNs). It then explains the evolution of CNN and how it provides complicated object detection in images. It also explains how to build an image classifier using CNN in Python.
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Downloading the code bundle and coloured images: Please follow the link to download the Code Bundle and the Coloured Images of the book: https://rebrand.ly/9c16d0 Errata We take immense pride in our work at BPB Publications and follow best practices to ensure the accuracy of our content to provide with an indulging reading experience to our subscribers. Our readers are our mirrors, and we use their inputs to reflect and improve upon human errors, if any, that may have occurred during the publishing processes involved. To let us maintain the quality and help us reach out to any readers who might be having difficulties due to any unforeseen errors, please write to us at : errata@bpbonline.com Your support, suggestions and feedbacks are highly appreciated by the BPB Publications’ Family.
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Did you know that BPB offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.bpbonline.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at business@bpbonline.com for more details. At you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on BPB books and eBooks.
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BPB is searching for authors like you If you're interested in becoming an author for BPB, please visit www.bpbonline.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea. The code bundle for the book is also hosted on GitHub at In case there's an update to the code, it will be updated on the existing GitHub repository. We also have other code bundles from our rich catalog of books and videos available at Check them out! PIRACY If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at business@bpbonline.com with a link to the material. If you are interested in becoming an author
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If there is a topic that you have expertise in, and you are interested in either writing or contributing to a book, please visit REVIEWS Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at BPB can understand what you think about our products, and our authors can see your feedback on their book. Thank you! For more information about BPB, please visit
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Table of Contents 1. Introduction to AI and Python Introduction Structure Objectives Introduction to Artificial Intelligence (AI) Why to learn AI? Understanding intelligence Types of intelligence Various fields of study in AI Applications of AI in various industries How does artificial intelligence learn? AI agents and environments What is an agent? What is an agent’s environment? AI and Python – how do they relate? What is Python? Why choose Python for building AI applications? Python3 – installation and setup Windows Linux Ubuntu Linux Mint CentOS Fedora Installing and compiling Python from Source macOS/Mac OS X Conclusion Questions
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2. Machine Learning and Its Algorithms Introduction Structure Objectives Understanding Machine Learning (ML) The Landscape of Machine Learning Algorithms Components of a Machine Learning algorithm Different learning styles in machine learning algorithms Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning Popular machine learning algorithms Linear regression Logistic regression Decision tree algorithm Random forest Naïve Bayes algorithm Support Vector Machine (SVM) k-Nearest Neighbor (kNN) K-Means clustering Conclusion Questions 3. Classification and Regression Using Supervised Learning Introduction Structure Objectives Classification
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Various steps to build a classifier using Python Step 1 – Import ML library Step 2 – Import dataset Step 3 – Organizing data-training and testing set Step 4 – Creating ML model Step 5 – Train the model Step 6 – Predicting test set result Step 7 – Evaluating the accuracy Lazy earning versus eager learning Performance metrics for classification Confusion matrix Accuracy Precision Recall Specificity F1 score Regression Various steps to build a regressor using Python Step 1 – Import ML library Step 2 – Import dataset Step 3 – Organizing data into training and testing set Step 4 – Creating ML model Step 5 – Train the model Step 6 – Plotting the regression line Step 7 – Calculating the variance Performance metrics for regression Mean Absolute Error (MAE) Mean Squared Error (MSE) R-Squared (R2) Adjusted R-squared (R2)
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Conclusion Questions 4. Clustering Using Unsupervised Learning Introduction Structure Objectives Clustering Various methods to form clusters Important ML clustering algorithms K-means clustering algorithm Mean-shift clustering algorithm Hierarchical clustering algorithm Performance metrics for clustering Silhouette analysis Davies–Bouldin index Dunn index Conclusion Questions 5. Solving Problems with Logic Programming Introduction Structure Objectives Logic programming Building blocks of logic programming Useful Python packages for logic programming Implementation examples Checking and generating prime numbers Solving the puzzles
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Conclusion Questions 6. Natural Language Processing with Python Introduction Structure Objective Natural Language Processing (NLP) Working of NLP Phases/logical steps in NLP Implementing NLP Installing Python’s NLTK Package Installing NLTK Downloading NLTK corpus Understanding tokenization, stemming, and lemmatization Tokenization Stemming Lemmatization Difference between lemmatization and stemming Understanding chunking Importance of chunking Understanding Bag-of-Words (BoW) model Why the BoW algorithm? Implementing the BoW algorithm using Python Understanding stop words When to remove stop words? Removing stop words using the NLTK library Understanding vectorization and transformers Vectorization techniques Transformers
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