Explore the most innovative and cutting-edge machine learning techniques with Scala.
- 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
Who is this course intended for?
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.
|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 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|
|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|
|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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