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ISBN: 978-66-30-27738-8
Copyright © Nidhi Sharma, Honey Singh, Ajay Sharma, Deepak Dagar Copyright © 2026 Dodo Books Indian Ocean Ltd. and OmniScriptum S.R.L publishing group
CONTENTS CHAPTER 1..........................................................................................1 INTRODUCTION TO ARTIFICIAL INTELLIGENCE ...........................1
1.1 Definition and Scope of Artificial Intelligence.....................................3 1.2 History and Evolution of Artificial Intelligence....................................6 1.3 Applications of AI in Modern Society...............................................14 CHAPTER 2........................................................................................27 FUNDAMENTALS OF MACHINE LEARNING....................................27
2.1 Concepts and Types of Machine Learning.........................................29 2.2 Supervised, Unsupervised, and Reinforcement Learning.....................37 2.3 Real-World Uses of Machine Learning.............................................48 CHAPTER 3........................................................................................59 MATHEMATICS FOR AI AND ML......................................................59
3.1 Linear Algebra Basics....................................................................61 3.2 Probability and Statistics................................................................71 3.3 Calculus in Machine Learning.........................................................84 CHAPTER 4........................................................................................95 DATA COLLECTION AND PREPROCESSING....................................95
4.1 Sources of Data.............................................................................96 4.2 Data Cleaning and Transformation................................................. 109 4.3 Feature Selection and Engineering................................................. 123 i
CHAPTER 5...................................................................................... 129 SUPERVISED LEARNING ALGORITHMS....................................... 129
5.1 Regression Techniques................................................................. 130 5.2 Classification Methods................................................................. 137 5.3 Model Evaluation Metrics ............................................................ 146 CHAPTER 6...................................................................................... 151 UNSUPERVISED LEARNING TECHNIQUES ................................... 151
6.1 Clustering Algorithms.................................................................. 152 6.2 Dimensionality Reduction ............................................................ 156 6.3 Association Rule Learning............................................................ 159 CHAPTER 7...................................................................................... 163 NEURAL NETWORKS AND DEEP LEARNING................................ 163
7.1 Structure of Neural Networks........................................................ 164 7.2 Activation Functions and Training ................................................. 173 7.3 Introduction to Deep Learning....................................................... 178 CHAPTER 8...................................................................................... 182 NATURAL LANGUAGE PROCESSING............................................. 182
8.1 Text Processing Techniques .......................................................... 183 8.2 Sentiment Analysis and Chatbots................................................... 188 8.3 Language Translation Systems...................................................... 194 ii
CHAPTER 9...................................................................................... 200 COMPUTER VISION........................................................................ 200
9.1 Image Processing Fundamentals.................................................... 201 9.2 Object Detection and Recognition.................................................. 206 9.3 Applications of Computer Vision................................................... 210 CHAPTER 10.................................................................................... 216 REINFORCEMENT LEARNING....................................................... 216
10.1 Basics of Reinforcement Learning................................................ 217 10.2 Reward Systems and Agents........................................................ 222 10.3 Applications in Robotics and Gaming........................................... 226 CHAPTER 11.................................................................................... 230 AI TOOLS AND FRAMEWORKS...................................................... 230
11.1 Python for AI Development......................................................... 231 11.2 TensorFlow and PyTorch ............................................................ 235 11.3 Cloud-Based AI Platforms........................................................... 239 CHAPTER 12.................................................................................... 243 ETHICAL ISSUES IN AI.................................................................... 243
12.1 Bias and Fairness in AI............................................................... 244 12.2 Privacy and Security Concerns .................................................... 247 12.3 Responsible AI Development...................................................... 250 iii
CHAPTER 13.................................................................................... 254 AI IN INDUSTRY AND BUSINESS .................................................... 254
13.1 AI in Healthcare........................................................................ 255 13.2 AI in Finance and Marketing....................................................... 260 13.3 AI in Education and Manufacturing.............................................. 266 CHAPTER 14.................................................................................... 271 FUTURE TRENDS IN AI AND ML..................................................... 271
14.1 Explainable AI.......................................................................... 273 14.2 Generative AI Technologies ........................................................ 277 14.3 Human-AI Collaboration............................................................ 283 CHAPTER 15.................................................................................... 289 CHALLENGES AND OPPORTUNITIES IN AI................................... 289
15.2 Career Opportunities in AI and ML.............................................. 293 15.2 Career Opportunities in AI and ML.............................................. 297 15.3 Future Research Directions......................................................... 300 iv