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Machine Learning with Scala Quick Start Guide - Leverage popular machine learning algorithms and techniques and implement them in Scala (Paperback)
Loot Price: R880
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Machine Learning with Scala Quick Start Guide - Leverage popular machine learning algorithms and techniques and implement them in Scala (Paperback)
Expected to ship within 10 - 15 working days
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Supervised and unsupervised machine learning made easy in Scala
with this quick-start guide. Key Features Construct and deploy
machine learning systems that learn from your data and give
accurate predictions Unleash the power of Spark ML along with
popular machine learning algorithms to solve complex tasks in
Scala. Solve hands-on problems by combining popular neural network
architectures such as LSTM and CNN using Scala with DeepLearning4j
library Book DescriptionScala is a highly scalable integration of
object-oriented nature and functional programming concepts that
make it easy to build scalable and complex big data applications.
This book is a handy guide for machine learning developers and data
scientists who want to develop and train effective machine learning
models in Scala. The book starts with an introduction to machine
learning, while covering deep learning and machine learning basics.
It then explains how to use Scala-based ML libraries to solve
classification and regression problems using linear regression,
generalized linear regression, logistic regression, support vector
machine, and Naive Bayes algorithms. It also covers tree-based
ensemble techniques for solving both classification and regression
problems. Moving ahead, it covers unsupervised learning techniques,
such as dimensionality reduction, clustering, and recommender
systems. Finally, it provides a brief overview of deep learning
using a real-life example in Scala. What you will learn Get
acquainted with JVM-based machine learning libraries for Scala such
as Spark ML and Deeplearning4j Learn RDDs, DataFrame, and Spark SQL
for analyzing structured and unstructured data Understand
supervised and unsupervised learning techniques with best practices
and pitfalls Learn classification and regression analysis with
linear regression, logistic regression, Naive Bayes, support vector
machine, and tree-based ensemble techniques Learn effective ways of
clustering analysis with dimensionality reduction techniques Learn
recommender systems with collaborative filtering approach Delve
into deep learning and neural network architectures Who this book
is forThis book is for machine learning developers looking to train
machine learning models in Scala without spending too much time and
effort. Some fundamental knowledge of Scala programming and some
basics of statistics and linear algebra is all you need to get
started with this book.
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