- Python Machine Learning Second Edition
- Table of Contents
- Python Machine Learning Second Edition
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Packt is Searching for Authors Like You
- 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
- Introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- Summary
- 2. Training Simple Machine Learning Algorithms for Classification
- 3. A Tour of Machine Learning Classifiers Using scikit-learn
- Choosing a classification algorithm
- First steps with scikit-learn – training a perceptron
- 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 linear regression
- 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
- Dealing with nonlinear relationships using random forests
- Summary
- 11. Working with Unlabeled Data – Clustering Analysis
- 12. Implementing a Multilayer Artificial Neural Network from Scratch
- 13. Parallelizing Neural Network Training with TensorFlow
- 14. Going Deeper – The Mechanics of TensorFlow
- Key features of TensorFlow
- TensorFlow ranks and tensors
- Understanding TensorFlow's computation graphs
- Placeholders in TensorFlow
- Variables in TensorFlow
- Building a regression model
- Executing objects in a TensorFlow graph using their names
- Saving and restoring a model in TensorFlow
- Transforming Tensors as multidimensional data arrays
- Utilizing control flow mechanics in building graphs
- Visualizing the graph with TensorBoard
- Summary
- 15. Classifying Images with Deep Convolutional Neural Networks
- 16. Modeling Sequential Data Using Recurrent Neural Networks
- Introducing sequential data
- RNNs for modeling sequences
- Implementing a multilayer RNN for sequence modeling in TensorFlow
- Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs
- Project two – implementing an RNN for character-level language modeling in TensorFlow
- Chapter and book summary
- Index
Jared Huffman is an entrepreneur, gamer, storyteller, machine learning fanatic, and database aficionado. He has dedicated the past 10 years to developing software and analyzing data. His previous work has spanned a variety of topics, including network security, financial systems, and business intelligence, as well as web services, developer tools, and business strategy. Most recently, he was the founder of the data science team at Minecraft, with a focus on big data and machine learning. When not working, you can typically find him gaming or enjoying the beautiful Pacific Northwest with friends and family.
I'd like to thank Packt for giving me the opportunity to work on such a great book, my wife for the constant encouragement, and my daughter for sleeping through most of the late nights while I was reviewing and debugging code.
Huai-En, Sun (Ryan Sun) holds a master's degree in statistics from the National Chiao Tung University. He is currently working as a data scientist for analyzing the production line at PEGATRON. Machine learning and deep learning are his main areas of research.
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