scikit-learn Cookbook
scikit-learn has evolved as a robust library for machine learning applications in python with support for a wide range of supervised and unsupervised learning algorithms. This edition brings to you the various enhancements to its model implementations, API and bug fixes in the latest major release of scikit-learn to support Python. This book covers easy to follow recipes right from mathematical operations to implementing various supervised, unsupervised and deep learning algorithms with scikit-learn. Get practical hands-on knowledge to implement various models and algorithms like Multi-Layer Perceptrons, time-series split, MAE criterion for regression, criteria for gradient boosting, Classifier, Regressor, and much more.
Offered by
Difficulty Level
Intermediate
Completion Time
12h28m
Language
English
About Book
Who Is This Book 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.
scikit-learn Cookbook
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h28m total length
High-Performance Machine Learning: Numpy
Premodel Workflow & Preprocessing
Dimensionality Reduction
Linear Models with scikit-learn
Linear Models: Logistic Regression
Building Models with Distance Metrics
Cross-Validation & Post Model Workflow
Support Vector Machines
Tree Algorithms and Ensembles
Text and Multi-Class Classification with scikit-learn
Neural Networks
Create a Simple Estimator
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