Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

by Chris Albon

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. You'll find recipes

Genres: Programming, Technology, Computer Science, Textbooks, Artificial Intelligence, Computers

364 pages, Paperback

Published May 1, 2018

Reviews

[Mahmoud Rabie · Rating 4 out of 5]

A very good book on “How to do machine learning”, the book don’t explain any topics in deep but only shows how to do different stuff using python libraries, mainly scikit-learn and keras

The book contains 21 chapters and each chapter contains a number of “Problem - Solution - Code - Discussion” sections

All the book sections have the same style, “Problem” (i.e. Handling Imbalanced Classes in Support Vector Machines), “Solution” (i.e. Increase the penalty for misclassifying the smaller class using class_weight) and then few lines of code to show solution, then a discussion section that explain the solution and any alternative approaches

The book contains 183 different problem that cover a lot of topics (i.e. Data Wrangling, Handling Numerical Data, Handling Categorical Data, Handling Text, Handling Dates and Times, Handling Images, Dimensionality Reduction Using Feature Extraction, Model Evaluation, Model Selection, Linear Regression, Trees and Forests, K-Nearest Neighbors)

The book can be a very good reference for the new Data Scientists and will save a lot of time on their daily activities

[Reza Rahutomo · Rating 5 out of 5]

Chris Albon helped my early days at my job as data engineer back in 2016 through his website. And all practical knowledge of him is compiled in this book. It is recommended for those who love learning python data science through practice and straight explanation.

[Stein Karlsen · Rating 5 out of 5]

Great book with excellent examples and discussion

[Karl · Rating 5 out of 5]

Excellent handbook. Not for beginners.

[Vinay Mittal · Rating 5 out of 5]

The best starter kit for machine learning.

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Accession No: DL030124, DL030175-81