Learning Paths

Follow curated learning paths based on your goals and experience level.

ML Fundamentals Track

Beginner

Start here if you're new to machine learning. Covers math foundations through classical ML algorithms.

Chapters Included:

Chapter 1: Linear Algebra for ML
Chapter 2: Calculus and Optimization
Chapter 3: Probability and Statistics
Chapter 4: Python Data Science Stack
Chapter 5: Supervised Learning Fundamentals
Chapter 6: Classification Algorithms
Chapter 8: Unsupervised Learning

Deep Learning Practitioner

Intermediate

For those with ML basics who want to master deep learning and neural networks.

Chapters Included:

Chapter 9: Neural Network Foundations
Chapter 10: Training Deep Networks
Chapter 11: Convolutional Neural Networks
Chapter 12: Sequence Models
Chapter 13: Transformer Architecture

LLM & NLP Specialist

Advanced

Focus on large language models, transformers, and natural language processing.

Chapters Included:

Chapter 13: Transformer Architecture
Chapter 14: Pre-trained Language Models
Chapter 15: Fine-tuning and Alignment
Chapter 16: Efficient Transformer Architectures
Chapter 23: Retrieval-Augmented Generation
Chapter 24: Agentic AI Systems

Computer Vision Track

Intermediate

Learn image processing, CNNs, vision transformers, and multimodal models.

Chapters Included:

Chapter 11: Convolutional Neural Networks
Chapter 17: Vision Transformers
Chapter 20: Diffusion Models
Chapter 21: Vision-Language Foundations
Chapter 22: Text-to-Image and Beyond

MLOps & Production

Intermediate

Deploy models to production, track experiments, and build scalable ML systems.

Chapters Included:

Chapter 7: Ensemble Methods
Chapter 26: MLOps and Production Systems
Chapter 27: AI Ethics and Responsible Development
Appendix A: Environment Setup Guide
Appendix B: GPU Computing Fundamentals
Appendix C: Cloud Platform Quick Start