About the book

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, Handling text or numerical data, model selection, and dimensionality reduction and many other topics.
Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

Content:

1. Vectors, Matrices and Arrays
2. Loading Data
3. Data Wrangling
4. Handling Numerical Data
5. Handling Categorical Data
6. Handling Text
7. Handling Dates and Times
8. Handling Images
9. Dimensionality Reduction Using Feature Extraction
10. Dimensionality Reduction Using Feature Selection
11. Model Evaluation
12. Model Selection
13. Linear Regression
14. Trees and Forests
15. K-Nearest Neighbors
16. Logistic Regression
17. Support Vector Machines
18. Naïve Bayes
19. Clustering Neural Networks
20. Saving and Loading Trained Models