Book Image

Python Machine Learning By Example - Second Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Second Edition

By: Yuxi (Hayden) Liu

Overview of this book

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Fundamentals of Machine Learning
Section 1: Fundamentals of Machine Learning
2
Getting Started with Machine Learning and Python
Getting Started with Machine Learning and Python
Defining machine learning and why we need it
A very high-level overview of machine learning technology
Core of machine learning – generalizing with data
Preprocessing, exploration, and feature engineering
Combining models
Installing software and setting up
Summary
Exercises
3
Section 2: Practical Python Machine Learning By Example
Section 2: Practical Python Machine Learning By Example
4
Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
How computers understand language - NLP
Picking up NLP basics while touring popular NLP libraries
Getting the newsgroups data
Exploring the newsgroups data
Thinking about features for text data
Visualizing the newsgroups data with t-SNE
Summary
Exercises
5
Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
Learning without guidance – unsupervised learning
Clustering newsgroups data using k-means
Discovering underlying topics in newsgroups
Topic modeling using NMF
Topic modeling using LDA
Summary
Exercises
6
Detecting Spam Email with Naive Bayes
Detecting Spam Email with Naive Bayes
Getting started with classification
Exploring Naïve Bayes
Classification performance evaluation
Model tuning and cross-validation
Summary
Exercise
7
Classifying Newsgroup Topics with Support Vector Machines
Classifying Newsgroup Topics with Support Vector Machines
Finding separating boundary with support vector machines
Classifying newsgroup topics with SVMs
More example – fetal state classification on cardiotocography
A further example – breast cancer classification using SVM with TensorFlow
Summary
Exercise
8
Predicting Online Ad Click-Through with Tree-Based Algorithms
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
Implementing a decision tree from scratch
Predicting ad click-through with decision tree
Ensembling decision trees – random forest
Summary
Exercise
9
Predicting Online Ad Click-Through with Logistic Regression
Predicting Online Ad Click-Through with Logistic Regression
Converting categorical features to numerical – one-hot encoding and ordinal encoding
Classifying data with logistic regression
Training a logistic regression model
Training on large datasets with online learning
Handling multiclass classification
Implementing logistic regression using TensorFlow
Feature selection using random forest
Summary
Exercises
10
Scaling Up Prediction to Terabyte Click Logs
Scaling Up Prediction to Terabyte Click Logs
Learning the essentials of Apache Spark
Programming in PySpark
Learning on massive click logs with Spark
Feature engineering on categorical variables with Spark
Summary
Exercises
11
Stock Price Prediction with Regression Algorithms
Stock Price Prediction with Regression Algorithms
Brief overview of the stock market and stock prices
What is regression?
Mining stock price data
Estimating with linear regression
Estimating with decision tree regression
Estimating with support vector regression
Estimating with neural networks
Evaluating regression performance
Predicting stock price with four regression algorithms
Summary
Exercise
12
Section 3: Python Machine Learning Best Practices
Section 3: Python Machine Learning Best Practices
13
Machine Learning Best Practices
Machine Learning Best Practices
Machine learning solution workflow
Best practices in the data preparation stage
Best practices in the training sets generation stage
Best practices in the model training, evaluation, and selection stage
Best practices in the deployment and monitoring stage
Summary
Exercises
14
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Handling multiclass classification

One last thing worth noting is how logistic regression algorithms deal with multiclass classification. Although we interact with the scikit-learn classifiers in multiclass cases the same way as in binary cases, it is encouraging to understand how logistic regression works in multiclass classification.

Logistic regression for more than two classes is also called multinomial logistic regression, or better known latterly as softmax regression. As we have seen in the binary case, the model is represented by one weight vector w, the probability of the target being 1 or the positive class is written as follows:

In the K class case, the model is represented by K weight vectors, w1, w2, …, wK, and the probability of the target being class k is written as follows:

Note that the term normalizes probabilities (k from 1 to K) so that they total...

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