Learning scikit-learn: Machine Learning in Python

Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level.
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Learning scikit-learn: Machine Learning in Python

Raúl Garreta, Guillermo Moncecchi

1 customer reviews
Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level.
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Book Details

ISBN 139781783281930
Paperback118 pages

Book Description

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data”, huge amounts of information is being generated. This makes machine learning an unavoidable source of new data-based approximations for problem solving.

With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python.

The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.

You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem.

With Learning scikit-learn: Machine Learning in Python you will learn how to use the Python programming language and the scikit-learn library to build applications that learn from experience, applying the main concepts and techniques of machine learning.

Table of Contents

Chapter 1: Machine Learning – A Gentle Introduction
Installing scikit-learn
Our first machine learning method –linear classification
Evaluating our results
Machine learning categories
Important concepts related to machine learning
Summary
Chapter 2: Supervised Learning
Image recognition with Support Vector Machines
Text classification with Naïve Bayes
Explaining Titanic hypothesis with decision trees
Predicting house prices with regression
Summary
Chapter 3: Unsupervised Learning
Principal Component Analysis
Clustering handwritten digits with k-means
Alternative clustering methods
Summary
Chapter 4: Advanced Features
Feature extraction
Feature selection
Model selection
Grid search
Parallel grid search
Summary

What You Will Learn

  • Set up scikit-learn inside your Python environment
  • Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naïve Bayes
  • Use Decision Trees to explain the main causes of certain phenomenon such as the Titanic passengers’ survival
  • Predict house prices using regression techniques
  • Display and analyse groups in your data using dimensionality reduction
  • Make use of different tools to preprocess, extract, and select the learning features
  • Select the best parameters for your models using model selection
  • Improve the way you build your models using parallelization techniques

Authors

Table of Contents

Chapter 1: Machine Learning – A Gentle Introduction
Installing scikit-learn
Our first machine learning method –linear classification
Evaluating our results
Machine learning categories
Important concepts related to machine learning
Summary
Chapter 2: Supervised Learning
Image recognition with Support Vector Machines
Text classification with Naïve Bayes
Explaining Titanic hypothesis with decision trees
Predicting house prices with regression
Summary
Chapter 3: Unsupervised Learning
Principal Component Analysis
Clustering handwritten digits with k-means
Alternative clustering methods
Summary
Chapter 4: Advanced Features
Feature extraction
Feature selection
Model selection
Grid search
Parallel grid search
Summary

Book Details

ISBN 139781783281930
Paperback118 pages
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