Practical Machine Learning

Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Practical Machine Learning

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Sunila Gollapudi

8 customer reviews
Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials
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Book Details

ISBN 139781784399689
Paperback468 pages

Book Description

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data.

This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application.

With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data.

You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.

Table of Contents

Chapter 1: Introduction to Machine learning
Machine learning
Performance measures
Some complementing fields of Machine learning
Machine learning process lifecycle and solution architecture
Machine learning algorithms
Machine learning tools and frameworks
Summary
Chapter 2: Machine learning and Large-scale datasets
Big data and the context of large-scale Machine learning
Algorithms and Concurrency
Technology and implementation options for scaling-up Machine learning
Summary
Chapter 3: An Introduction to Hadoop's Architecture and Ecosystem
Introduction to Apache Hadoop
Machine learning solution architecture for big data (employing Hadoop)
Hadoop 2.x
Summary
Chapter 4: Machine Learning Tools, Libraries, and Frameworks
Machine learning tools – A landscape
Apache Mahout
R
Julia
Python
Apache Spark
Spring XD
Summary
Chapter 5: Decision Tree based learning
Decision trees
Implementing Decision trees
Summary
Chapter 6: Instance and Kernel Methods Based Learning
Instance-based learning (IBL)
Kernel methods-based learning
Summary
Chapter 7: Association Rules based learning
Association rules based learning
Implementing Apriori and FP-growth
Summary
Chapter 8: Clustering based learning
Clustering-based learning
Types of clustering
The k-means clustering algorithm
Implementing k-means clustering
Summary
Chapter 9: Bayesian learning
Bayesian learning
Implementing Naïve Bayes algorithm
Summary
Chapter 10: Regression based learning
Regression analysis
Regression methods
Implementing linear and logistic regression
Summary
Chapter 11: Deep learning
Background
Deep learning taxonomy
Implementing ANNs and Deep learning methods
Summary
Chapter 12: Reinforcement learning
Reinforcement Learning (RL)
Reinforcement learning solution methods
Summary
Chapter 13: Ensemble learning
Ensemble learning methods
Implementing ensemble methods
Summary
Chapter 14: New generation data architectures for Machine learning
Evolution of data architectures
Emerging perspectives & drivers for new age data architectures
Modern data architectures for Machine learning
Summary

What You Will Learn

  • Implement a wide range of algorithms and techniques for tackling complex data
  • Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
  • Harness the capabilities of Spark and Hadoop to manage and process data successfully
  • Apply the appropriate machine learning technique to address real-world problems
  • Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning
  • Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more

Authors

Table of Contents

Chapter 1: Introduction to Machine learning
Machine learning
Performance measures
Some complementing fields of Machine learning
Machine learning process lifecycle and solution architecture
Machine learning algorithms
Machine learning tools and frameworks
Summary
Chapter 2: Machine learning and Large-scale datasets
Big data and the context of large-scale Machine learning
Algorithms and Concurrency
Technology and implementation options for scaling-up Machine learning
Summary
Chapter 3: An Introduction to Hadoop's Architecture and Ecosystem
Introduction to Apache Hadoop
Machine learning solution architecture for big data (employing Hadoop)
Hadoop 2.x
Summary
Chapter 4: Machine Learning Tools, Libraries, and Frameworks
Machine learning tools – A landscape
Apache Mahout
R
Julia
Python
Apache Spark
Spring XD
Summary
Chapter 5: Decision Tree based learning
Decision trees
Implementing Decision trees
Summary
Chapter 6: Instance and Kernel Methods Based Learning
Instance-based learning (IBL)
Kernel methods-based learning
Summary
Chapter 7: Association Rules based learning
Association rules based learning
Implementing Apriori and FP-growth
Summary
Chapter 8: Clustering based learning
Clustering-based learning
Types of clustering
The k-means clustering algorithm
Implementing k-means clustering
Summary
Chapter 9: Bayesian learning
Bayesian learning
Implementing Naïve Bayes algorithm
Summary
Chapter 10: Regression based learning
Regression analysis
Regression methods
Implementing linear and logistic regression
Summary
Chapter 11: Deep learning
Background
Deep learning taxonomy
Implementing ANNs and Deep learning methods
Summary
Chapter 12: Reinforcement learning
Reinforcement Learning (RL)
Reinforcement learning solution methods
Summary
Chapter 13: Ensemble learning
Ensemble learning methods
Implementing ensemble methods
Summary
Chapter 14: New generation data architectures for Machine learning
Evolution of data architectures
Emerging perspectives & drivers for new age data architectures
Modern data architectures for Machine learning
Summary

Book Details

ISBN 139781784399689
Paperback468 pages
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