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Python Machine Learning, Second Edition

Python Machine Learning, Second Edition

By : Sebastian Raschka, Vahid Mirjalili
4.3 (89)
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Python Machine Learning, Second Edition

Python Machine Learning, Second Edition

4.3 (89)
By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published. Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities. If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Table of Contents (18 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Giving Computers the Ability to Learn from Data
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
2. Training Simple Machine Learning Algorithms for Classification
2. Training Simple Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
Implementing a perceptron learning algorithm in Python
Adaptive linear neurons and the convergence of learning
Summary
3
3. A Tour of Machine Learning Classifiers Using scikit-learn
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
4. Building Good Training Sets – Data Preprocessing
4. Building Good Training Sets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset into separate training and test sets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Summary
5
5. Compressing Data via Dimensionality Reduction
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5. Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
Supervised data compression via linear discriminant analysis
Using kernel principal component analysis for nonlinear mappings
Summary
6
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Streamlining workflows with pipelines
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
Dealing with class imbalance
Summary
7
7. Combining Different Models for Ensemble Learning
7. Combining Different Models for Ensemble Learning
Learning with ensembles
Combining classifiers via majority vote
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Summary
8
8. Applying Machine Learning to Sentiment Analysis
8. Applying Machine Learning to Sentiment Analysis
Preparing the IMDb movie review data for text processing
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
Topic modeling with Latent Dirichlet Allocation
Summary
9
9. Embedding a Machine Learning Model into a Web Application
9. Embedding a Machine Learning Model into a Web Application
Serializing fitted scikit-learn estimators
Setting up an SQLite database for data storage
Developing a web application with Flask
Turning the movie review classifier into a web application
Deploying the web application to a public server
Summary
10
10. Predicting Continuous Target Variables with Regression Analysis
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
11. Working with Unlabeled Data – Clustering Analysis
11. Working with Unlabeled Data – Clustering Analysis
Grouping objects by similarity using k-means
Organizing clusters as a hierarchical tree
Locating regions of high density via DBSCAN
Summary
12
12. Implementing a Multilayer Artificial Neural Network from Scratch
12. Implementing a Multilayer Artificial Neural Network from Scratch
Modeling complex functions with artificial neural networks
Classifying handwritten digits
Training an artificial neural network
About the convergence in neural networks
A few last words about the neural network implementation
Summary
13
13. Parallelizing Neural Network Training with TensorFlow
13. Parallelizing Neural Network Training with TensorFlow
TensorFlow and training performance
Training neural networks efficiently with high-level TensorFlow APIs
Choosing activation functions for multilayer networks
Summary
14
14. Going Deeper – The Mechanics of 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
15. Classifying Images with Deep Convolutional Neural Networks
15. Classifying Images with Deep Convolutional Neural Networks
Building blocks of convolutional neural networks
Putting everything together to build a CNN
Implementing a deep convolutional neural network using TensorFlow
Summary
16
16. Modeling Sequential Data Using Recurrent 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
17
Index
Index

Supervised data compression via linear discriminant analysis

Linear Discriminant Analysis (LDA) can be used as a technique for feature extraction to increase the computational efficiency and reduce the degree of overfitting due to the curse of dimensionality in non-regularized models.

The general concept behind LDA is very similar to PCA. Whereas PCA attempts to find the orthogonal component axes of maximum variance in a dataset, the goal in LDA is to find the feature subspace that optimizes class separability. In the following sections, we will discuss the similarities between LDA and PCA in more detail and walk through the LDA approach step by step.

Principal component analysis versus linear discriminant analysis

Both PCA and LDA are linear transformation techniques that can be used to reduce the number of dimensions in a dataset; the former is an unsupervised algorithm, whereas the latter is supervised. Thus, we might intuitively think that LDA is a superior feature extraction technique...

End of Chapter 5
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