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Explore the most innovative and cutting-edge machine learning techniques with Scala.

Course Description

The ability to apply machine learning techniques to large datasets is becoming a highly sought-after skill in the world of technology. Scala can help you deliver key insights into your data—its unique capabilities as a language let you build sophisticated algorithms and statistical models. For this reason, machine learning and Scala fit together perfectly and knowledge of both would be beneficial for anyone entering the data science field.
The course starts with a general introduction to the Scala programming language. From there, you’ll be introduced to several practical machine learning algorithms from the areas of exploratory data analysis. You’ll look at supervised learning machine learning models for prediction and classification tasks, and unsupervised learning techniques such as clustering and dimensionality reduction and neural networks.
By the end, you will be comfortable applying machine learning algorithms to solve real-world problems using Scala.

Learning Outcomes

  • Write Scala code implementing neural network models for prediction and clustering
  • Plot and analyze the structure of datasets with exploratory data analysis techniques using Scala
  • Use new and popular Scala frameworks such as Akka and Spark to implement machine learning algorithms and Scala libraries such as Breeze for numerical computing and plotting
  • Get to grips with the most popular machine learning algorithms used in the areas of regression, classification, clustering, dimensionality reduction, and neural networks
  • Use the power of MLlib libraries to implement machine learning with Spark
  • Work with the k-means algorithm and implement it in Scala with the real datasets
  • Get to know what dimensionality reduction is and explore the theory behind how the PCA algorithm works
  • Analyze and implement linear regression and GLMs in Scala and run them on real datasets
  • Use the Naive bayes algorithms and its methods to predict the probability of different classes based on various attributes


Prior knowledge of one of the JVM languages and basic knowledge in math and statistics is required.

Who is this course intended for?

This course is for those who wish to make sense of their complex data and get hidden insights from it. If you want to build smarter, more accurate Scala applications, this course is for you.

Your Instructor

Packt Publishing

Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages to cutting-edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.

From skills that will help you to develop and future-proof your career to immediate solutions to everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Introduction to Scala
The Course Overview 00:00:00
Functional Combinators in Scala 00:00:00
Scala Traits, Classes, and Objects 00:00:00
IntelliJ IDEA as an IDE 00:00:00
The Breeze Library for Linear Algebra 00:00:00
WISP for Plotting 00:00:00
Exploratory Data Analysis with Scala
Exploratory Data Analysis 00:00:00
Using DataFrames with Scala and Plotting with Breeze 00:00:00
Supervised Learning
Supervised Learning Problem Formulation 00:00:00
Two Basic Regression Algorithms 00:00:00
Implementing Linear Regression and GLMs in Scala 00:00:00
Two Basic Classification Algorithms 00:00:00
Implementing K-Nearest Neighbors and Naive Bayes in Scala 00:00:00
Model Selection 00:00:00
Unsupervised Learning
Unsupervised Learning Problem Formulation 00:00:00
Implementing K-means Algorithm in Scala 00:00:00
Mixture of Gaussians Clustering 00:00:00
Implementing Mixture of Gaussians Clustering in Scala 00:00:00
Dimensionality Reduction with Principle Component Analysis (PCA) 00:00:00
Implementing PCA in Scala 00:00:00
Neural Networks
Introduction to Feed-Forward Neural Networks 00:00:00
Implementing the Feed-Forward Neural Network in Scala 00:00:00
Introduction to Restricted Boltzmann Machines (RBMs) 00:00:00
Implementing Restricted Boltzmann Machines in Scala 00:00:00
Other Scala Frameworks for Machine Learning
The Akka Actor Model for Concurrency 00:00:00
A Multi-threaded K-Nearest Neighbors Implementation with Akka 00:00:00
Introduction to Apache Spark 00:00:00
Running Linear Regression on Spark with MLlib 00:00:00

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