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 ContentsPart I: Introduction & Preliminary RequirementsChapter 1: Basic ConceptsChapter 2: VisualizationChapter 3: Probability and StatisticsPart II: Unsupervised LearningChapter 4: ClusteringChapter 5: Frequent Itemset, Sequence Mining and Information RetrievalPart III: Data EngineeringChapter 6: Feature EngineeringChapter 7: Dimensionality Reduction and Data DecompositionPart IV: Supervised LearningChapter 8: Regression AnalysisChapter 9: ClassificationPart V: Neural NetworkChapter 10: Neural Networks and Deep LearningChapter 11: Self-Supervised Deep LearningChapter 12: Deep Learning Models and Applications (Text, Vision, and Audio)Part VI: Reinforcement LearningChapter 13: Reinforcement LearningPart VII: Other Algorithms and ConceptsChapter 14: Making Lighter Neural Network and Machine Learning ModelsChapter 15: Graph Mining AlgorithmsChapter 16: Concepts and Challenges of Working with Data