- 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
The execution of the code examples provided in this book requires an installation of Python 3.6.0 or newer on macOS, 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; moreover, 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 TensorFlow for efficient neural network training on graphical processing units (Chapters 13 to 16).
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