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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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