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

Publisher:Packt Publishing Limited

By: Sunila Gollapudi

Paid access

|Jun 2024

E-Book €32.99Institutions €132.95

Description

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge techniques

Key Features

Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
Comprehensive practical solutions taking you into the future of machine learning
Go a step further and integrate your machine learning projects with Hadoop

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.

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

Who this book is for

This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.

Introduction to Machine learning
Context of Large datasets for Machine learning
Hadoop as a Machine learning platform
ML tools and frameworks (R, Mahout, Julia, Spark and Python)
Decision Tree learning methods
Instance based & Kernel learning methods (KNN and SVM)
Association rule based learning methods (Apriori& FP-growth)
Clustering based learning methods (K-means)
Supervised & Unsupervised Learning: Linear Methods
Unsupervised Learning: Clustering Methods
Deep Learning Methods
Reinforcement learning
Summary of all the large scale machine learning frameworks and tools
Looking Ahead: Lamda Architectures, Polyglot Persistence and Semantic Data Platforms for Machine Learning

https://github.com/packtpublishing/practical-machine-learning

PDF ISBN: 978-1-78439-401-1

Publisher: Packt Publishing Limited

Publication date: 2024

Language: English

Pages: 468

Related subjects:

Computer sciences,