O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Learning scikit-learn: Machine Learning in Python

Book Description

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.

  • Use Python and scikit-learn to create intelligent applications
  • Apply regression techniques to predict future behaviour and learn to cluster items in groups by their similarities
  • Make use of classification techniques to perform image recognition and document classification

In Detail

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

  1. Learning scikit-learn: Machine Learning in Python
    1. Table of Contents
    2. Learning scikit-learn: Machine Learning in Python
    3. Credits
    4. About the Authors
    5. About the Reviewers
    6. www.PacktPub.com
      1. Support files, eBooks, discount offers and more
        1. Why Subscribe?
        2. Free Access for Packt account holders
    7. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
        1. Downloading the example code
        2. Errata
        3. Piracy
        4. Questions
    8. 1. Machine Learning – A Gentle Introduction
      1. Installing scikit-learn
        1. Linux
        2. Mac
        3. Windows
        4. Checking your installation
        5. Datasets
      2. Our first machine learning method –linear classification
      3. Evaluating our results
      4. Machine learning categories
      5. Important concepts related to machine learning
      6. Summary
    9. 2. Supervised Learning
      1. Image recognition with Support Vector Machines
        1. Training a Support Vector Machine
      2. Text classification with Naïve Bayes
        1. Preprocessing the data
        2. Training a Naïve Bayes classifier
        3. Evaluating the performance
      3. Explaining Titanic hypothesis with decision trees
        1. Preprocessing the data
        2. Training a decision tree classifier
        3. Interpreting the decision tree
        4. Random Forests – randomizing decisions
        5. Evaluating the performance
      4. Predicting house prices with regression
        1. First try – a linear model
        2. Second try – Support Vector Machines for regression
        3. Third try – Random Forests revisited
        4. Evaluation
      5. Summary
    10. 3. Unsupervised Learning
      1. Principal Component Analysis
      2. Clustering handwritten digits with k-means
      3. Alternative clustering methods
      4. Summary
    11. 4. Advanced Features
      1. Feature extraction
      2. Feature selection
      3. Model selection
      4. Grid search
      5. Parallel grid search
      6. Summary
    12. Index