Mastering Machine Learning with scikit-learn
This book examines machine learning models including k-nearest neighbors, logistic regression, naive Bayes, random forests, and support vector machines. You will work through document classification, image recognition, and other example problems.
Offered by
Difficulty Level
Intermediate
Completion Time
8h28m
Language
English
About Book
Who Is This Book For?
This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required.
Mastering Machine Learning with scikit-learn
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 8h28m total length
The Fundamentals of Machine Learning
Simple linear regression
Classification and Regression with K Nearest Neighbors
Feature Extraction and Preprocessing
From Simple Regression to Multiple Regression
From Linear Regression to Logistic Regression
Naive Bayes
Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests, and other Ensemble Methods
The Perceptron
From the Perceptron to Support Vector Machines
From the Perceptron to Artificial Neural Networks
Clustering with K-Means
Dimensionality Reduction with Principal Component Analysis
Related Resources
Access Ready-to-Use Books for Free!
Get instant access to a library of pre-built books—free trial, no credit card required. Start training your team in minutes!