Books

Python Machine Learning Cookbook

python machine learning cookbook

Python Machine Learning Cookbook

PRACTICAL SOLUTIONS FROM PREPROCESSING TO DEEP LEARNING

Python Machine Learning Cookbook: Over the last few years, machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. As the popularity of machine learning increased, a cottage industry of high-quality literature that taught applied machine learning to practitioners developed. This literature has been highly successful in training an entire generation of data scientists and machine learning engineers. This literature also approached the topic of machine learning from the perspective of providing a learning resource to teach an individual what machine learning is and how it works. However, while fruitful, this approach left out a different perspective on the topic: the nuts and bolts of doing machine learning day to day.

That is the motivation of this book not as a tome of machine learning knowledge for the student but as a wrench for the professional, to sit with dog-eared pages on desks ready to solve the practical day-to-day problems of a machine learning practitioner. More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into.

Content of Python Machine Learning Cookbook

  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. Naive Bayes
  19. Clustering
  20. Neural Networks
  21. Saving and Loading Trained Models

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