Jupyter Notebooks

Download runnable companion notebooks for the chapters. Each notebook bundles the chapter's Python code into executable cells.

Part 1: Mathematical Foundations for Machine Learning

Chapter 1: Linear Algebra for ML
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Chapter 2: Calculus and Optimization
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Chapter 3: Probability and Statistics
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Part 2: Python Ecosystem and Classical Machine Learning

Chapter 4: Python Data Science Stack
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Chapter 5: Supervised Learning Fundamentals
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Chapter 6: Classification Algorithms
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Chapter 7: Ensemble Methods
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Chapter 8: Unsupervised Learning
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Part 3: Deep Learning Fundamentals

Chapter 9: Neural Network Foundations
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Chapter 10: Training Deep Networks
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Chapter 11: Convolutional Neural Networks
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Chapter 12: Sequence Models
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Part 4: Modern Architectures and Large Language Models

Chapter 13: Transformer Architecture
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Chapter 14: Pre-trained Language Models
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Chapter 15: Fine-tuning and Alignment
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Chapter 16: Efficient Transformer Architectures
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Chapter 17: Vision Transformers
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Part 5: Generative Models

Chapter 18: Variational Autoencoders
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Chapter 19: Generative Adversarial Networks
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Chapter 20: Diffusion Models
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Part 6: Multimodal AI and Vision-Language Models

Chapter 21: Vision-Language Foundations
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Chapter 22: Text-to-Image and Beyond
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Part 7: Applied AI Systems

Chapter 23: Retrieval-Augmented Generation
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Chapter 24: Agentic AI Systems
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Chapter 25: Reinforcement Learning
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Chapter 26: MLOps and Production Systems
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Chapter 27: AI Ethics and Responsible Development
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Part 8: Data Science: Process & Practice

Chapter 28: The Data Science Lifecycle
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Chapter 29: Data Acquisition & Wrangling
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Chapter 30: Exploratory Data Analysis
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Chapter 31: Feature Engineering & Selection
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Chapter 32: Data Visualization & Storytelling
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Part 9: Statistics & Statistical Inference

Chapter 33: Descriptive Statistics & Distributions
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Chapter 34: Probability Distributions in Depth
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Chapter 35: Sampling & Estimation
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Chapter 36: Hypothesis Testing
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Chapter 37: Resampling & the Bootstrap
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Chapter 38: Bayesian Inference
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Chapter 39: Regression & Generalized Linear Models
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Chapter 40: Experimental Design & A/B Testing
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Part 10: Databases & SQL for Data Science

Chapter 41: Relational Databases & SQL Fundamentals
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Chapter 42: Intermediate SQL: Joins & Aggregation
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Chapter 43: Advanced SQL: Window Functions & CTEs
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Chapter 44: Analytical SQL & Data Warehousing
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Chapter 45: NoSQL & Modern Data Stores
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Part 11: Time Series Analysis & Forecasting

Chapter 46: Time Series Fundamentals
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Chapter 47: Classical Forecasting: ARIMA & ETS
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Chapter 48: Machine Learning for Time Series
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Part 12: Programming for Data Science

Chapter 49: Python for Data Science in Depth
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Chapter 51: Reproducible Research & Notebooks
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