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Machine Learning for Data Mining

By : Jesus Salcedo

5 (2)

Buy this Book

Machine Learning for Data Mining

5 (2)

By: Jesus Salcedo

Buy this Book

Overview of this book

Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques. This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this book, you will be able to build predictive models and extract information of interest from the dataset

Preface

Who this book is for

What this book covers

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Free Chapter

Introducing Machine Learning Predictive Models

Characteristics of machine learning predictive models

Types of machine learning predictive models

Working with neural networks

A sample neural network model

Summary

Getting Started with Machine Learning

Demonstrating a neural network

Support Vector Machines

Demonstrating SVMs

Summary

Understanding Models

Models

Using graphs to interpret machine learning models

Using statistics to interpret machine learning models

Using decision trees to interpret machine learning models

Summary

Improving Individual Models

Modifying model options

Using a different model to improve results

Removing noise to improve models

Doing additional data preparation

Balancing data

Summary

Advanced Ways of Improving Models

Combining models

Using propensity scores

Meta-level modeling

Error modeling

Boosting and bagging

Predicting continuous outcomes

Summary

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