Python Machine Learning By Example

By : Yuxi (Hayden) Liu

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

Preface

What this book covers

What you need for this book

Free Chapter

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

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

Summary

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

Summary

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

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

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

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

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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Python Machine Learning By Example

By : Yuxi (Hayden) Liu

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

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