Download e-book for iPad: Mastering Machine Learning with scikit-learn - Second by Gavin Hackeling

Key Features

• Master well known computing device studying versions together with k-nearest acquaintances, random forests, logistic regression, k-means, naive Bayes, and synthetic neural networks
• Learn the right way to construct and overview functionality of effective versions utilizing scikit-learn
• Practical consultant to grasp your fundamentals and examine from actual existence functions of desktop learning

Book Description

Machine studying is the buzzword bringing computing device technological know-how and statistics jointly to construct shrewdpermanent and effective versions. utilizing strong algorithms and methods provided through desktop studying you could automate any analytical model.

This e-book examines a number of desktop studying versions together with well known laptop studying algorithms equivalent to k-nearest buddies, logistic regression, naive Bayes, k-means, choice bushes, and synthetic neural networks. It discusses information preprocessing, hyperparameter optimization, and ensemble tools. you are going to construct structures that classify records, realize photos, become aware of advertisements, and extra. you are going to learn how to use scikit-learn's API to extract beneficial properties from express variables, textual content and pictures; review version functionality, and boost an instinct for a way to enhance your model's performance.

By the tip of this e-book, you are going to grasp all required recommendations of scikit-learn to construct effective types at paintings to hold out complicated projects with the sensible approach.

What you are going to learn

• Review basic thoughts resembling bias and variance
• Extract beneficial properties from express variables, textual content, and images
• Predict the values of continuing variables utilizing linear regression and ok Nearest Neighbors
• Classify records and photographs utilizing logistic regression and aid vector machines
• Create ensembles of estimators utilizing bagging and boosting techniques
• Discover hidden constructions in info utilizing K-Means clustering
• Evaluate the functionality of desktop studying structures in universal tasks

About the Author

Gavin Hackeling is a knowledge scientist and writer. He used to be labored on a number of computing device studying difficulties, together with computerized speech acceptance, record class, item popularity, and semantic segmentation. An alumnus of the college of North Carolina and big apple college, he lives in Brooklyn together with his spouse and cat.

Table of Contents

1. The basics of computing device Learning
2. Simple linear regression
3. Classification and Regression with ok Nearest Neighbors
4. Feature Extraction and Preprocessing
5. From uncomplicated Regression to a number of Regression
6. From Linear Regression to Logistic Regression
7. Naive Bayes
8. Nonlinear category and Regression with choice Trees
9. From determination timber to Random Forests, and different Ensemble Methods
10. The Perceptron
11. From the Perceptron to aid Vector Machines
12. From the Perceptron to synthetic Neural Networks
13. Clustering with K-Means
14. Dimensionality aid with central part Analysis