About the Book

Machine Learning With Python Cookbook: Practical Solutions from Pre-processing to Deep Learning 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.

Contents:

Vectors, Matrices and Arrays

Loading Data

Data Wrangling    

Handling Numerical Data

Handling Categorical Data

Handling Text

Handling Dates and Times

Handling Images

Dimensionality Reduction Using Feature Extraction

Dimensionality Reduction Using Feature selection

Model Evaluation

Model Selection

Linear Regression

Trees and Forests

K-Nearest Neighbors

Logistic Regression

Support Vector Machines

Naïve Bayes

Clustering

Neural Networks

Saving and Loading Trained Models