Python Machine Learning By Example

Python Machine Learning By Example
Yuxi (Hayden) Liu
1: Getting Started with Python and Machine Learning
Chapter 1: Getting Started with Python and Machine Learning
What is machine learning and why do we need it?
A very high level overview of machine learning
A brief history of the development of machine learning algorithms
Generalizing with data
Overfitting, underfitting and the bias-variance tradeoff
Avoid overfitting with feature selection and dimensionality reduction
Preprocessing, exploration, and feature engineering
Combining models
Installing software and setting up
Troubleshooting and asking for help
Summary
2: Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
Chapter 2: Exploring the 20 Newsgroups Dataset with Text Analysis
Algorithms
What is NLP?
Touring powerful NLP libraries in Python
The newsgroups data
Getting the data
Thinking about features
Visualization
Data preprocessing
Clustering
Topic modeling
Summary
3: Spam Email Detection with Naive Bayes
Chapter 3: Spam Email Detection with Naive Bayes
Getting started with classification
Types of classification
Applications of text classification
Exploring naive Bayes
Bayes’ theorem by examples
The mechanics of naive Bayes
The naive Bayes implementations
Classifier performance evaluation
Model tuning and cross-validation
Contents
sSummary
4: News Topic Classification with Support Vector Machine
Chapter 4: News Topic Classification with Support Vector Machine
Recap and inverse document frequency
Support vector machine
News topic classification with support vector machine
More examples – fetal state classification on cardiotocography with
SVM
Summary
5: Click-Through Prediction with Tree-Based Algorithms
Chapter 5: Click-Through Prediction with Tree-Based Algorithms
Brief overview of advertising click-through prediction
Getting started with two types of data, numerical and categorical
Decision tree classifier
Click-through prediction with decision tree
Random forest – feature bagging of decision tree
Summary
6: Click-Through Prediction with Logistic Regression
Chapter 6: Click-Through Prediction with Logistic Regression
One-hot encoding – converting categorical features to numerical
Logistic regression classifier
Click-through prediction with logistic regression by gradient descent
Feature selection via random forest
Summary
7: Stock Price Prediction with Regression Algorithms
Chapter 7: Stock Price Prediction with Regression Algorithms
Brief overview of the stock market and stock price
What is regression?
Predicting stock price with regression algorithms
Summary
8: Best Practices
Chapter 8: Best Practices
Machine learning 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
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