From the Book - Second edition.
Part I: Jupyter: Beyond normal Python
1. Geting startein in IPython and Jupyter
2. Enhanced interactive features
3. Debugging and profiling
Part II: Introduction to NumPy
4. Understanding data types in Python
5. The basics of NumPy arrays
6. Computation on NumPy arrays: Universal functions
7. Aggregations: min, max, and everything in between
8. Computation on arrays: broadcasting
9. Comparisons, masks, and boolean logic
12. Structured data: NumPy's structured arrays
Part III: Data manipulation with Pandas
13. Introducing Pandas objects
14. Data indexing and selection
15. Operating on data in Pandas
16. Handling missing data
18. Combining datasets: concat and append
19. Combining datasets: merge and join
20. Aggregation and grouping
22. Vectorized string operations
23. Working with time series
24. High-performace Pandas: eval and query
Part IV: Visualization with Matplotlib
25. General Matplotlib tips
28. Density and contour plots
29. Customizing plot legends
30. Customizing colorbars
34. Customizing Matplotlib: Configurations and stylesheets
35. Three-dimensional plottin in Matplotlib
36. Visualization with Seaborn
37. What is machine learning?
38. Introducing Scitit-Learn
39. Hyperparameters and model validation
41. In depth: Naive beyes classification
42. In depth: Linear regression
43. In depth: Support vector machines
44. In depth: Decision trees and random forests
45. In depth: Principal component analysis
46. In depth: Manifold learning
47. In depth: k-means clustering
48. In depth: Gaussian mixture models
49. In depth: Kernel density estimation
50. Application: a face detection pipeline.
