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Machine Learning for Data Mining电子书

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60人正在读 | 0人评论 6.7

作       者:Jesus Salcedo

出  版  社:Packt Publishing

出版时间:2019-04-30

字       数:10.9万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features * Learn how to apply machine learning techniques in the field of data science * Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results * A step-by-step approach to improving model development and performance Book Description 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 What you will learn * Hone your model-building skills and create the most accurate models * Understand how predictive machine learning models work * Prepare your data to acquire the best possible results * Combine models in order to suit the requirements of different types of data * Analyze single and multiple models and understand their combined results * Derive worthwhile insights from your data using histograms and graphs Who this book is for If you are a data scientist, data analyst, and data mining professional and are keen to achieve a 30% higher salary by adding machine learning to your skillset, then this is the ideal book for you. You will learn to apply machine learning techniques to various data mining challenges. No prior knowledge of machine learning is assumed.
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Preface

Who this book is for

What this book covers

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Conventions used

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Reviews

Introducing Machine Learning Predictive Models

Characteristics of machine learning predictive models

Types of machine learning predictive models

Working with neural networks

Advantages of neural networks

Disadvantages of neural networks

Representing the errors

Types of neural network models

Multi-layer perceptron

Why are weights important?

An example representation of a multilayer perceptron model

The linear regression model

A sample neural network model

Feed-forward backpropagation

Model training ethics

Summary

Getting Started with Machine Learning

Demonstrating a neural network

Running a neural network model

Interpreting results

Analyzing the accuracy of the model

Model performance on testing partition

Support Vector Machines

Working with Support Vector Machines

Kernel transformation

But what is the best solution?

Types of kernel functions

Demonstrating SVMs

Interpreting the results

Trying additional solutions

Summary

Understanding Models

Models

Statistical models

Decision tree models

Machine learning models

Using graphs to interpret machine learning models

Using statistics to interpret machine learning models

Understanding the relationship between a continuous predictor and a categorical outcome variable

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

How to remove noise

Doing additional data preparation

Preparing the data

Balancing data

The need for balancing data

Implementing balance in data

Summary

Advanced Ways of Improving Models

Combining models

Combining by voting

Combining by highest confidence

Implementing combining models

Combining models in Modeler

Combining models outside Modeler

Using propensity scores

Implementations of propensity scores

Meta-level modeling

Error modeling

Boosting and bagging

Boosting

Bagging

Predicting continuous outcomes

Summary

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