This book serves as a guiding tool in your exploration of AI applications. In this second edition, we have meticulously curated ten chapters, each focusing on distinct facets of AI, designed to equip you with fundamental knowledge and practical skills essential for your journey in computer engineering. Chapter 1 introduces you to the foundational concepts of Artificial Intelligence, offering an overview of its history, principles, and applications. Building upon this, Chapter 2 delves deeper into Machine Learning, the cornerstone of AI, elucidating its algorithms and methodologies. Chapters 3, 4, and 5 focus on different paradigms of Supervised and Unsupervised Learning. You will explore Unsupervised Learning with a spotlight on the clustering algorithm and Supervised Learning through regression and classification, providing you with practical insights into their implementation and utility. Reinforcement Learning, covered in Chapter 6, unveils the mechanisms behind learning through interaction and feedback, highlighting its significance in various AI applications. Chapter 7 navigates you through the intricate world of Deep Learning, elucidating neural networks and their applications in solving complex problems.
Natural Language Processing (NLP), discussed in Chapter 8, opens doors to understanding how AI comprehends and processes human language, while Chapter 9, focusing on Computer Vision, unravels the mechanisms behind AI-powered image recognition and analysis. Finally, in Chapter 10, we address an essential aspect often overlooked in AI discussions – AI Ethics. This chapter stimulates critical thinking about the moral implications and responsible use of AI technology in society.

CHAPTER 1 INTRODUCTION TO ARTIFICIAL INTELLIGENCE........................1
1.1 Artificial Intelligence Definition …………………….........................................1
1.2 Difference Between AI, ML, and Deep Learning …………………………......2
1.3 Three Levels of AI …………………..……………………………...……....….2
1.4 Machine Learning Definition.………………………………… ……...………..2
1.5 Examples of AI Applications ………………...…………………..……………3
1.6 Practices and exercises ………………...………..………………..……………3
CHAPTER 2 MACHINE LEARNING.............................................................................6
2.1 Machine Learning Definition…………………..................................................6
2.2 Machine Learning Workflow……………..……………....................................7
2.3 Methods of Machine Learning………………………………….………..….....8
2.4 Algorithms of Machine Learning.……….……………………………. ……..12
2.5 Machine Learning Evaluation………………………….…………….…….…13
2.6 Machine Learning with Python Libraries………………………….……….…14
2.7 Practices and exercises………………………….………………………….…15
CHAPTER 3 UNSUPERVISED LEARNING | CLUSTERING...................................18
3.1 Unsupervised Learning Definition……………….…………………….……...18
3.2 Algorithms of Clustering………………………….………………………..…19
3.3 Clustering Applications……………………………………………..………...26
3.4 Practices and exercises…………………………………...………….……..…28
CHAPTER 4 SUPERVISED LEARNING | REGRESSION.........................................30
4.1 Supervised Learning Definition……………….………….……………....…...30
4.2 Algorithms of Regression………………………………………...………...…32
4.3 Regression Applications…………………………………………...……...…..38
4.4 Practices and exercises………………………..………………..……..………40
CHAPTER 5 SUPERVISED LEARNING | CLASSIFICATION................................42
5.1 Classification Definition……………….……………………………………...42
5.2 Algorithms of Classification………………………………...……………...…44
5.3 Classification Applications……………………………..………………...…...51
5.4 Practices and exercises………………………………………………..…….…53
CHAPTER 6 REINFORCEMENT LEARNING...........................................................56
6.1 Reinforcement Learning Definition…..………………………………………56
6.2 Algorithms of Reinforcement Learning…..………………………....….….…58
6.3 Reinforcement Learning Applications….…………………………..….……..65
6.4 Practices and exercises ………..………..…..……………………….……..…66
CHAPTER 7 DEEP LEARNING....................................................................................70
7.1 Deep Learning Definition ……..………………………………….….....……70
7.2 Algorithm of Deep Learning …………………………………………...….…74
7.3 Deep Learning Applications…………………………...……………....……..79
7.4 Practices and exercises………………………………………………...……...81
CHAPTER 8 NATURAL LANGUAGE PROCESSING..............................................84
8.1 Natural Language Processing Definition ……………………………….……84
8.2 Natural Language Processing Process …………………………..….……..…86
8.3 Natural Language Processing Applications ……………………………..…...90
8.4 Practices and exercises………………………………………………...……...92
CHAPTER 9 COMPUTER VISION...............................................................................95
9.1 Computer Vision Definition……………………………………..……………95
9.2 Computer Vision Process ……...……………..………………………..……..96
9.3 Computer Vision Applications ………..………………………..…...………103
9.4 Practices and exercises ………………………..……………………….........106
CHAPTER 10 AI ETHICS.............................................................................................108
10.1 AI Ethics Definition …….……………….…………………………..….....108
10.2 AI Ethics Process ……….………………………………………..……..…113
10.3 AI Ethics Real-World Applications ……………………………………….115
10.4 Practices and exercises………………………..…………………….…..….116