Machine learning with Python cookbook : practical solutions from preprocessing to deep learning / Chris Albon.

Includes index.

Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit 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 -- Naive bayes -- Clustering -- Neural networks -- Saving and loading trained models.

With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. --