Python Machine Learning By Example by Hayden -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Python Machine Learning By Example Second Edition

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Dedication Foreword Contributors

About the author About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Section 1: Fundamentals of Machine Learning Getting Started with Machine Learning and Python

Defining machine learning and why we need it A very high-level overview of machine learning technology

Types of machine learning tasks A brief history of the development of machine learning algorithms

Core of machine learning – generalizing with data

Overfitting, underfitting, and the bias-variance trade-off Avoiding overfitting with cross-validation Avoiding overfitting with regularization Avoiding overfitting with feature selection and dimensionality reduction

Preprocessing, exploration, and feature engineering

Missing values Label encoding One hot encoding Scaling Polynomial features Power transform Binning

Combining models

Voting and averaging Bagging Boosting Stacking

Installing software and setting up

Setting up Python and environments Installing the various packages

NumPy SciPy Pandas Scikit-learn TensorFlow

Summary Exercises

Section 2: Practical Python Machine Learning By Example Exploring the 20 Newsgroups Dataset with Text Analysis Techniques

How computers understand language - NLP Picking up NLP basics while touring popular NLP libraries

Corpus Tokenization PoS tagging Named-entity recognition Stemming and lemmatization Semantics and topic modeling

Getting the newsgroups data Exploring the newsgroups data Thinking about features for text data

Counting the occurrence of each word token Text preprocessing Dropping stop words Stemming and lemmatizing words

Visualizing the newsgroups data with t-SNE

What is dimensionality reduction? t-SNE for dimensionality reduction

Summary Exercises

Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms

Learning without guidance – unsupervised learning Clustering newsgroups data using k-means

How does k-means clustering work? Implementing k-means from scratch Implementing k-means with scikit-learn Choosing the value of k Clustering newsgroups data using k-means

Discovering underlying topics in newsgroups Topic modeling using NMF Topic modeling using LDA Summary Exercises

Detecting Spam Email with Naive Bayes

Getting started with classification

Types of classification Applications of text classification

Exploring Naïve Bayes

Learning Bayes' theorem by examples The mechanics of Naïve Bayes Implementing Naïve Bayes from scratch Implementing Naïve Bayes with scikit-learn

Classification performance evaluation Model tuning and cross-validation Summary Exercise

Classifying Newsgroup Topics with Support Vector Machines

Finding separating boundary with support vector machines

Understanding how SVM works through different use cases

Case 1 – identifying a separating hyperplane Case 2 – determining the optimal hyperplane Case 3 – handling outliers

Implementing SVM

Case 4 – dealing with more than two classes

The kernels of SVM

Case 5 – solving linearly non-separable problems

Choosing between linear and RBF kernels

Classifying newsgroup topics with SVMs More example – fetal state classification on cardiotocography A further example – breast cancer classification using SVM with TensorFlow Summary Exercise

Predicting Online Ad Click-Through with Tree-Based Algorithms

Brief overview of advertising click-through prediction Getting started with two types of data – numerical and categorical Exploring decision tree from root to leaves

Constructing a decision tree The metrics for measuring a split

Implementing a decision tree from scratch Predicting ad click-through with decision tree Ensembling decision trees – random forest

Implementing random forest using TensorFlow

Summary Exercise

Predicting Online Ad Click-Through with Logistic Regression

Converting categorical features to numerical – one-hot encoding and ordinal encoding Classifying data with logistic regression

Getting started with the logistic function Jumping from the logistic function to logistic regression

Training a logistic regression model

Training a logistic regression model using gradient descent Predicting ad click-through with logistic regression using gradient descent Training a logistic regression model using stochastic gradient descent Training a logistic regression model with regularization

Training on large datasets with online learning Handling multiclass classification Implementing logistic regression using TensorFlow Feature selection using random forest Summary Exercises

Scaling Up Prediction to Terabyte Click Logs

Learning the essentials of Apache Spark

Breaking down Spark Installing Spark Launching and deploying Spark programs

Programming in PySpark Learning on massive click logs with Spark

Loading click logs Splitting and caching the data One-hot encoding categorical features Training and testing a logistic regression model

Feature engineering on categorical variables with Spark

Hashing categorical features Combining multiple variables – feature interaction

Summary Exercises

Stock Price Prediction with Regression Algorithms

Brief overview of the stock market and stock prices What is regression? Mining stock price data

Getting started with feature engineering Acquiring data and generating features

Estimating with linear regression

How does linear regression work? Implementing linear regression

Estimating with decision tree regression

Transitioning from classification trees to regression trees Implementing decision tree regression Implementing regression forest

Estimating with support vector regression

Implementing SVR

Estimating with neural networks

Demystifying neural networks Implementing neural networks

Evaluating regression performance Predicting stock price with four regression algorithms Summary Exercise

Section 3: Python Machine Learning Best Practices Machine Learning Best Practices

Machine learning solution workflow Best practices in the data preparation stage

Best practice 1 – completely understanding the project goal Best practice 2 – collecting all fields that are relevant Best practice 3 – maintaining the consistency of field values Best practice 4 – dealing with missing data Best practice 5 – storing large-scale data

Best practices in the training sets generation stage

Best practice 6 – identifying categorical features with numerical values Best practice 7 – deciding on whether or not to encode categorical features Best practice 8 – deciding on whether or not to select features, and if so, how to do so Best practice 9 – deciding on whether or not to reduce dimensionality, and if so, how to do so Best practice 10 – deciding on whether or not to rescale features Best practice 11 – performing feature engineering with domain expertise Best practice 12 – performing feature engineering without domain expertise Best practice 13 – documenting how each feature is generated Best practice 14 – extracting features from text data

Best practices in the model training, evaluation, and selection stage

Best practice 15 – choosing the right algorithm(s) to start with

Naïve Bayes Logistic regression SVM Random forest (or decision tree) Neural networks

Best practice 16 – reducing overfitting Best practice 17 – diagnosing overfitting and underfitting Best practice 18 – modeling on large-scale datasets

Best practices in the deployment and monitoring stage

Best practice 19 – saving, loading, and reusing models Best practice 20 – monitoring model performance Best practice 21 – updating models regularly

Summary Exercises

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