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Index
Cover Table of Contents Python: Deeper Insights into Machine Learning Python: Deeper Insights into Machine Learning Python: Deeper Insights into Machine Learning Credits Preface What you need for this learning path Who this learning path is for Reader feedback Customer support 1. Module 1 1. Giving Computers the Ability to Learn from Data 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 Implementing a perceptron learning algorithm in Python Adaptive linear neurons and the convergence of learning Summary 3. A Tour of Machine Learning Classifiers Using Scikit-learn 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 Handling categorical data Partitioning a dataset in training and test sets Bringing features onto the same scale Selecting meaningful features Assessing feature importance with random forests Summary 5. Compressing Data via Dimensionality Reduction Supervised data compression via linear discriminant analysis Using kernel principal component analysis for nonlinear mappings Summary 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning Using k-fold cross-validation to assess model performance Debugging algorithms with learning and validation curves Fine-tuning machine learning models via grid search Looking at different performance evaluation metrics Summary 7. Combining Different Models for Ensemble Learning Implementing a simple majority vote classifier Evaluating and tuning the ensemble classifier Bagging – building an ensemble of classifiers from bootstrap samples Leveraging weak learners via adaptive boosting Summary 8. Applying Machine Learning to Sentiment Analysis Introducing the bag-of-words model Training a logistic regression model for document classification Working with bigger data – online algorithms and out-of-core learning Summary 9. Embedding a Machine Learning Model into a Web Application Setting up a SQLite database for data storage Developing a web application with Flask Turning the movie classifier into a web application Deploying the web application to a public server Summary 10. Predicting Continuous Target Variables with Regression Analysis 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 Organizing clusters as a hierarchical tree Locating regions of high density via DBSCAN Summary 12. Training Artificial Neural Networks for Image Recognition 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 Choosing activation functions for feedforward neural networks Training neural networks efficiently using Keras Summary 2. Module 2 1. Thinking in Machine Learning Design principles Summary 2. Tools and Techniques IPython console Installing the SciPy stack NumPY Matplotlib Pandas SciPy Scikit-learn Summary 3. Turning Data into Information Big data Signals Cleaning data Visualizing data Summary 4. Models – Learning from Information Tree models Rule models Summary 5. Linear Models Logistic regression Multiclass classification Regularization Summary 6. Neural Networks Logistic units Cost function Implementing a neural network Gradient checking Other neural net architectures Summary 7. Features – How Algorithms See the World Operations and statistics Structured features Transforming features Principle component analysis Summary 8. Learning with Ensembles Bagging Boosting Ensemble strategies Summary 9. Design Strategies and Case Studies Model selection Learning curves Real-world case studies Machine learning at a glance Summary 3. Module 3 1. Unsupervised Machine Learning Introducing k-means clustering Self-organizing maps Further reading Summary 2. Deep Belief Networks Restricted Boltzmann Machine Deep belief networks Further reading Summary 3. Stacked Denoising Autoencoders Stacked Denoising Autoencoders Further reading Summary 4. Convolutional Neural Networks Further Reading Summary 5. Semi-Supervised Learning Understanding semi-supervised learning Semi-supervised algorithms in action Further reading Summary 6. Text Feature Engineering Text feature engineering Further reading Summary 7. Feature Engineering Part II Creating a feature set Feature engineering in practice Further reading Summary 8. Ensemble Methods Using models in dynamic applications Further reading Summary 9. Additional Python Machine Learning Tools Further reading Summary 10. Chapter Code Requirements A. Biblography Index
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