Textbook in PDF format

Unlock the power of Machine Learning with a guide designed to take you from foundational concepts to cutting-edge applications. Machine Learning: Algorithms, Theory, and Practice is your all-in-one companion for mastering the theory and hands-on techniques behind modern ML systems—crafted for students, developers, educators, and professionals alike. This comprehensive guide is structured for progressive learning. You’ll start with the essentials of AI and Python programming, then advance through data preprocessing, statistical modeling, and classical Machine Learning algorithms. From there, you'll dive into Deep Learning, natural language processing (NLP), reinforcement learning, and generative AI—each topic reinforced with real-world coding exercises and clear explanations. Inside, you’ll find Foundational theory and intuitive explanations of key ML concepts, including supervised and unsupervised learning, regression, classification, clustering, and model evaluation. Practical tutorials using Python and essential libraries such as NumPy, pandas, scikit-learn, and matplotlib. Practical tutorials that lead you through the process of building, training, and testing machine learning models Advanced coverage of neural networks, CNNs, RNNs, BERT, transformer models, and diffusion-based generative AI. Bonus content, including around 300 glossary terms, frequently asked questions, and hands-on guidance for using Jupyter Notebooks effectively. Whether you're aiming for AI certifications, transitioning into a Machine Learning role, or applying ML techniques to real-world challenges, this book provides both the conceptual clarity and practical skills to help you thrive in the evolving world of Machine Learning. Preface NTRODUCTION AI FUNDAMENTALS MACHINE LEARNING FUNDAMENTALS GETTING STARTED WITH PYTHON PYTHON FUNDAMENTALS FOR MACHINE LEARNING INTRODUCTION TO PYTHON LIBRARIES FOR MACHINE LEARNING NUMPY FOR MACHINE LEARNING PANDAS FOR MACHINE LEARNING ... ESSENTIAL MATHEMATICS FOR MACHINE LEARNING DATA PREPROCESSING SIMPLE LINEAR REGRESSION MULTIPLE LINEAR REGRESSION POLYNOMIAL REGRESSION LOGISTIC REGRESSION ... MODEL EVALUATION AND VALIDATION FEATURE SELECTION AND DIMENSIONALITY REDUCTION NEURAL NETWORKS DEEP LEARNING NATURAL LANGUAGE PROCESSING (NLP) REINFORCEMENT LEARNING GENERATIVE AI APPENDIX: FAQ APPENDIX: GLOSSARY OF ML TERMS APPENDIX: JUPYTER NOTEBOOK FOR MACHINE LEARNING