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Key benefits

Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala

Take your expertise in Scala programming to the next level by creating and customizing AI applications

Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style

Description

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You’ll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.

Who is this book for?

If you’re a data scientist or a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for you. All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

What you will learn

Build dynamic workflows for scientific computing

Leverage open source libraries to extract patterns from time series

Write your own classification, clustering, or evolutionary algorithm

Perform relative performance tuning and evaluation of Spark

Master probabilistic models for sequential data

Experiment with advanced techniques such as regularization and kernelization

Dive into neural networks and some deep learning architecture

Apply some basic multiarm-bandit algorithms

Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters

Apply key learning strategies to a technical analysis of financial markets

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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View plans & pricing

Frequently bought together

Scala: Guide for Data Science Professionals

€67.99

Scala for Machine Learning, Second Edition

€49.99

€53.99

Total € 171.97

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Dustin Nov 23, 2020

This book is truly excellent for learning how to apply advanced Scala features for ML purposes, and I think it should probably even have a subtitle that expresses this fact more clearly.You will probably want additional machine learning theory books, and you probably don’t want to take the finance examples too seriously.

Amazon Verified review

Spy Studios Jan 10, 2018

I've learned a lot from Nicholas's code already. He has a very unique style of programming. The only reason I didn't give the book five stars is because deciphering Nicholas's code can be a difficult task. It's obvious that Nicholas knows Scala extremely well. The only problem is, to understand Nicholas's code, you have to go so deep into the Scala programming language that you will waste a lot of time doing research.

Scala for Machine Learning, Second Edition: Build systems for data processing, machine learning, and deep learning , Second Edition

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