Mastering Machine Learning with scikit-learn

Apply effective learning algorithms to real-world problems using scikit-learn

Mastering Machine Learning with scikit-learn

Gavin Hackeling

1 customer reviews
Apply effective learning algorithms to real-world problems using scikit-learn
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Book Details

ISBN 139781783988365
Paperback238 pages

Book Description

This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.

You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.

By the end of the book, you will be an expert in scikit-learn and will be well versed in machine learning.

Table of Contents

Chapter 1: The Fundamentals of Machine Learning
Learning from experience
Machine learning tasks
Training data and test data
Performance measures, bias, and variance
An introduction to scikit-learn
Installing scikit-learn
Installing pandas and matplotlib
Summary
Chapter 2: Linear Regression
Simple linear regression
Evaluating the model
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Fitting models with gradient descent
Summary
Chapter 3: Feature Extraction and Preprocessing
Extracting features from categorical variables
Extracting features from text
Extracting features from images
Data standardization
Summary
Chapter 4: From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-label classification and problem transformation
Summary
Chapter 5: Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Decision trees with scikit-learn
Summary
Chapter 6: Clustering with K-Means
Clustering with the K-Means algorithm
Evaluating clusters
Image quantization
Clustering to learn features
Summary
Chapter 7: Dimensionality Reduction with PCA
An overview of PCA
Performing Principal Component Analysis
Using PCA to visualize high-dimensional data
Face recognition with PCA
Summary
Chapter 8: The Perceptron
Activation functions
Binary classification with the perceptron
Limitations of the perceptron
Summary
Chapter 9: From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Classifying characters in scikit-learn
Summary
Chapter 10: From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Approximating XOR with Multilayer perceptrons
Classifying handwritten digits
Summary

What You Will Learn

  • Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics
  • Predict the values of continuous variables using linear regression
  • Create representations of documents and images that can be used in machine learning models
  • Categorize documents and text messages using logistic regression and support vector machines
  • Classify images by their subjects
  • Discover hidden structures in data using clustering and visualize complex data using decomposition
  • Evaluate the performance of machine learning systems in common tasks
  • Diagnose and redress problems with models due to bias and variance

Authors

Table of Contents

Chapter 1: The Fundamentals of Machine Learning
Learning from experience
Machine learning tasks
Training data and test data
Performance measures, bias, and variance
An introduction to scikit-learn
Installing scikit-learn
Installing pandas and matplotlib
Summary
Chapter 2: Linear Regression
Simple linear regression
Evaluating the model
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Fitting models with gradient descent
Summary
Chapter 3: Feature Extraction and Preprocessing
Extracting features from categorical variables
Extracting features from text
Extracting features from images
Data standardization
Summary
Chapter 4: From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-label classification and problem transformation
Summary
Chapter 5: Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Decision trees with scikit-learn
Summary
Chapter 6: Clustering with K-Means
Clustering with the K-Means algorithm
Evaluating clusters
Image quantization
Clustering to learn features
Summary
Chapter 7: Dimensionality Reduction with PCA
An overview of PCA
Performing Principal Component Analysis
Using PCA to visualize high-dimensional data
Face recognition with PCA
Summary
Chapter 8: The Perceptron
Activation functions
Binary classification with the perceptron
Limitations of the perceptron
Summary
Chapter 9: From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Classifying characters in scikit-learn
Summary
Chapter 10: From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Approximating XOR with Multilayer perceptrons
Classifying handwritten digits
Summary

Book Details

ISBN 139781783988365
Paperback238 pages
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