
Machine Learning for Data Mining
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
- 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 DescriptionMachine 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 datasetWhat 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 forIf 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.
More details
Other editions
Additional editions

Content
- Intro
- Title Page
- Copyright and Credits
- Contributors
- About Packt
- Table of Contents
- Preface
- 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
- Other Books You May Enjoy
- Index
System requirements
File format: ePUB
Copy protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).
The file format ePub works well for novels and non-fiction books – i.e., „flowing” text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our ebook Help page.