- Python Machine Learning
- Table of Contents
- Python Machine Learning
- Credits
- Foreword
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Giving Computers the Ability to Learn from Data
- Building intelligent machines to transform data into knowledge
- The three different types of machine learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- Summary
- 2. Training Machine Learning Algorithms for Classification
- 3. A Tour of Machine Learning Classifiers Using Scikit-learn
- Choosing a classification algorithm
- First steps with scikit-learn
- Modeling class probabilities via logistic regression
- Maximum margin classification with support vector machines
- Solving nonlinear problems using a kernel SVM
- Decision tree learning
- K-nearest neighbors – a lazy learning algorithm
- Summary
- 4. Building Good Training Sets – Data Preprocessing
- 5. Compressing Data via Dimensionality Reduction
- 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- 7. Combining Different Models for Ensemble Learning
- 8. Applying Machine Learning to Sentiment Analysis
- 9. Embedding a Machine Learning Model into a Web Application
- 10. Predicting Continuous Target Variables with Regression Analysis
- Introducing a simple linear regression model
- Exploring the Housing Dataset
- Implementing an ordinary least squares linear regression model
- Fitting a robust regression model using RANSAC
- Evaluating the performance of linear regression models
- Using regularized methods for regression
- Turning a linear regression model into a curve – polynomial regression
- Summary
- 11. Working with Unlabeled Data – Clustering Analysis
- 12. Training Artificial Neural Networks for Image Recognition
- Modeling complex functions with artificial neural networks
- Classifying handwritten digits
- Training an artificial neural network
- Developing your intuition for backpropagation
- Debugging neural networks with gradient checking
- Convergence in neural networks
- Other neural network architectures
- A few last words about neural network implementation
- Summary
- 13. Parallelizing Neural Network Training with Theano
- Index
Author
Sebastian Raschka
Reviewers
Richard Dutton
Dave Julian
Vahid Mirjalili
Hamidreza Sattari
Dmytro Taranovsky
Commissioning Editor
Akram Hussain
Acquisition Editors
Rebecca Youe
Meeta Rajani
Content Development Editor
Riddhi Tuljapurkar
Technical Editors
Madhunikita Sunil Chindarkar
Taabish Khan
Copy Editors
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Project Coordinator
Kinjal Bari
Proofreader
Safis Editing
Indexer
Hemangini Bari
Graphics
Sheetal Aute
Abhinash Sahu
Production Coordinator
Shantanu N. Zagade
Cover Work
Shantanu N. Zagade
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