- 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
The execution of the code examples provided in this book requires an installation of Python 3.4.3 or newer on Mac OS X, Linux, or Microsoft Windows. We will make frequent use of Python's essential libraries for scientific computing throughout this book, including SciPy, NumPy, scikit-learn, matplotlib, and pandas.
The first chapter will provide you with instructions and useful tips to set up your Python environment and these core libraries. We will add additional libraries to our repertoire and installation instructions are provided in the respective chapters: the NLTK library for natural language processing (Chapter 8, Applying Machine Learning to Sentiment Analysis), the Flask web framework (Chapter 9, Embedding a Machine Learning Algorithm into a Web Application), the seaborn library for statistical data visualization (Chapter 10, Predicting Continuous Target Variables with Regression Analysis), and Theano for efficient neural network training on graphical processing units (Chapter 13, Parallelizing Neural Network Training with Theano).
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