Machine Learning and Artificial Intelligence
Concepts, Algorithms and Models
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Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources, statistics, and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper...
Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources, statistics, and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide. Furthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. Any resemblance between the jokes and real life is pure coincidence, and no offense is intended. Table of Contents 1. Part I: Introduction & Preliminary Requirements * Chapter 1: Basic Concepts * Chapter 2: Visualization * Chapter 3: Probability and Statistics 2. Part II: Unsupervised Learning * Chapter 4: Clustering * Chapter 5: Frequent Itemset, Sequence Mining and Information Retrieval 3. Part III: Data Engineering * Chapter 6: Feature Engineering * Chapter 7: Dimensionality Reduction and Data Decomposition 4. Part IV: Supervised Learning * Chapter 8: Regression Analysis * Chapter 9: Classification 5. Part V: Neural Network * Chapter 10: Neural Networks and Deep Learning * Chapter 11: Self-Supervised Deep Learning * Chapter 12: Deep Learning Models and Applications (Text, Vision, and Audio) 6. Part VI: Reinforcement Learning * Chapter 13: Reinforcement Learning 7. Part VII: Other Algorithms and Concepts * Chapter 14: Making Lighter Neural Network and Machine Learning Models * Chapter 15: Graph Mining Algorithms * Chapter 16: Concepts and Challenges of Working with Data
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