Mastering Machine Learning with scikit-learn - Second Edition

Use scikit-learn to apply machine learning to real-world problems
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Mastering Machine Learning with scikit-learn - Second Edition

Gavin Hackeling

Use scikit-learn to apply machine learning to real-world problems
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Book Details

ISBN 139781788299879
Paperback254 pages

Book Description

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.

This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.

By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

Table of Contents

Chapter 1: The Fundamentals of Machine Learning
Defining Machine Learning
Learning from Experience
Machine Learning Tasks
Training Data, Testing Data and Validation Data
Bias and Variance
An Introduction to scikit-learn
Installing scikit-learn
Installing pandas, Pillow and matplotlib
Summary
Chapter 2: Simple Linear Regression
Simple Linear Regression
Evaluating the Model
Summary
Chapter 3: Classification and Regression with K Nearest Neighbors
K-Nearest Neighbors
Lazy Learning and Non-Parametric Models
Classification with K-Nearest Neighbors
Regression with K-Nearest Neighbors
Summary
Chapter 4: Feature Extraction and Preprocessing
Extracting Features from Categorical Variables
Standardizing Features
Extracting Features from Text
Extracting Features from Images
Summary
Chapter 5: From Simple Regression to Multiple Regression
Multiple Linear Regression
Polynomial Regression
Regularization
Applying Linear Regression
Gradient Descent
Summary
Chapter 6: From Linear Regression to Logistic Regression
Binary Classification with Logistic Regression
Spam Filtering
Tuning Models with Grid Search
Multi-Class Classification
Multi-Label Classification and Problem Transformation
Summary
Chapter 7: Naive Bayes
Bayes' Theorem
Generative and Discriminative Models
Naive Bayes
Naive Bayes with scikit-learn
Summary
Chapter 8: Nonlinear Classification and Regression with Decision Trees
Decision Trees
Training Decision Trees
Gini Impurity
Chapter 9: From Decision Trees to Random Forests, and other Ensemble Methods
Bagging
Boosting
Stacking
Summary
Chapter 10: The Perceptron
The Perceptron
Limitations of the Perceptron
Summary
Chapter 11: 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 12: From the Perceptron to Artificial Neural Networks
Chapter 13: Clustering with K-Means

What You Will Learn

  • Review fundamental concepts such as bias and variance
  • Extract features from categorical variables, text, and images
  • Predict the values of continuous variables using linear regression and K Nearest Neighbors
  • Classify documents and images using logistic regression and support vector machines
  • Create ensembles of estimators using bagging and boosting techniques
  • Discover hidden structures in data using K-Means clustering
  • Evaluate the performance of machine learning systems in common tasks

Authors

Table of Contents

Chapter 1: The Fundamentals of Machine Learning
Defining Machine Learning
Learning from Experience
Machine Learning Tasks
Training Data, Testing Data and Validation Data
Bias and Variance
An Introduction to scikit-learn
Installing scikit-learn
Installing pandas, Pillow and matplotlib
Summary
Chapter 2: Simple Linear Regression
Simple Linear Regression
Evaluating the Model
Summary
Chapter 3: Classification and Regression with K Nearest Neighbors
K-Nearest Neighbors
Lazy Learning and Non-Parametric Models
Classification with K-Nearest Neighbors
Regression with K-Nearest Neighbors
Summary
Chapter 4: Feature Extraction and Preprocessing
Extracting Features from Categorical Variables
Standardizing Features
Extracting Features from Text
Extracting Features from Images
Summary
Chapter 5: From Simple Regression to Multiple Regression
Multiple Linear Regression
Polynomial Regression
Regularization
Applying Linear Regression
Gradient Descent
Summary
Chapter 6: From Linear Regression to Logistic Regression
Binary Classification with Logistic Regression
Spam Filtering
Tuning Models with Grid Search
Multi-Class Classification
Multi-Label Classification and Problem Transformation
Summary
Chapter 7: Naive Bayes
Bayes' Theorem
Generative and Discriminative Models
Naive Bayes
Naive Bayes with scikit-learn
Summary
Chapter 8: Nonlinear Classification and Regression with Decision Trees
Decision Trees
Training Decision Trees
Gini Impurity
Chapter 9: From Decision Trees to Random Forests, and other Ensemble Methods
Bagging
Boosting
Stacking
Summary
Chapter 10: The Perceptron
The Perceptron
Limitations of the Perceptron
Summary
Chapter 11: 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 12: From the Perceptron to Artificial Neural Networks
Chapter 13: Clustering with K-Means

Book Details

ISBN 139781788299879
Paperback254 pages
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