1. Introduction
2. About the Author
4. Month 1 — Foundations
5. 1. Overview
6. 2. Math foundations
7. 3. NumPy
8. 4. Pandas
9. 5. Matplotlib and Seaborn
10. 6. EDA Project (Capstone)
11. 7. Exercises
13. Month 2 — Classical ML (Scikit-learn)
14. 8. Overview
15. 9. Introduction to ML
16. 10. Regression
17. 11. Classification
18. 12. Clustering
19. 13. Feature Engineering
20. 14. Model Evaluation
21. 15. Ensemble Methods (XGBoost, LightGBM)
22. 16. Exercises
24. Month 3 — Deep Learning
25. 17. Overview
26. 18. Neural Network foundations
27. 19. PyTorch foundations
28. 20. TensorFlow and Keras
29. 21. Training techniques
30. 22. CNN — Convolutional Networks
31. 23. RNN, LSTM, GRU
32. 24. Exercises
34. Month 4 — Computer Vision + NLP
35. 25. Overview
36. 26. Introduction to Computer Vision
37. 27. Working with OpenCV
38. 28. YOLO and Object Detection
39. 29. NLP foundations
40. 30. Text Preprocessing
41. 31. Introduction to Transformers
42. 32. Exercises
44. Month 5 — LLM, RAG and AI Agents
45. 33. Overview
46. 34. LLM fundamentals
47. 35. Prompt Engineering
48. 36. OpenAI and Anthropic API
49. 37. LangChain and LlamaIndex
50. 38. Vector Databases
51. 39. RAG Pipeline
52. 40. AI Agents
53. 41. Fine-tuning (LoRA, QLoRA)
54. 42. Exercises
56. Month 6 — MLOps and Production
57. 43. Overview
58. 44. Introduction to MLOps
59. 45. MLflow — Experiment tracking
60. 46. DVC — Data Versioning
61. 47. FastAPI + ML Serving
62. 48. Docker and Kubernetes
63. 49. Model Monitoring
64. 50. CI/CD for ML
65. 51. Airflow and Prefect
66. 52. Exercises
68. Final Projects
69. 53. Portfolio Projects
70. 54. 1. Prediction API (Classical ML + FastAPI)
71. 55. 2. Computer Vision Service
72. 56. 3. RAG Chatbot
73. 57. 4. MLOps Pipeline (End-to-End)
75. Resources
76. 58. Resources list
77. 59. Books
78. 60. Online courses
79. 61. YouTube channels
80. 62. Datasets
81. 63. Cheatsheets
84. Glossary