scikit-learn Cookbook – Second Edition: Over 80 recipes for machine learning in Python with scikit-learn

Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications.

About This Book

• Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn
• Perform supervised and unsupervised learning with ease, and evaluate the performance of your model
• Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm

Who This Book Is For

Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book will help you too.

What You Will Learn

• Build predictive models in minutes by using scikit-learn
• Understand the differences and relationships between Classification and Regression, two types of Supervised Learning.
• Use distance metrics to predict in Clustering, a type of Unsupervised Learning
• Find points with similar characteristics with Nearest Neighbors.
• Use automation and cross-validation to find a best model and focus on it for a data product
• Choose among the best algorithm of many or use them together in an ensemble.
• Create your own estimator with the simple syntax of sklearn
• Explore the feed-forward neural networks available in scikit-learn

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.

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