更新时间：2021-07-02 12:42:13
coverpage
Title Page
Copyright and Credits
Python Machine Learning By Example Second Edition
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Dedication
Foreword
Contributors
About the author
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Preface
Who this book is for
What this book covers
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Download the example code files
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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
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
Discovering underlying topics in newsgroups
Topic modeling using NMF
Topic modeling using LDA
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
