Explore the most innovative and cutting edge machine learning techniques with Scala.
About This Machine Learning with Scala Video course
- Learn how to implement classification, regression, and clustering
- Discover key Scala machine learning libraries, what each library brings to the table, and what kind of problems each library is able to solve
- Dive deep into the world of analytics to predict situations correctly
Machine Learning with Scala In Detail
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.
Scala, Data Science training table of contents (duration : 1h58m)
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Introduction to Scala
- The Course Overview free 00:02:29
- Functional Combinators in Scala 00:04:33
- Scala Traits, Classes, and Objects 00:04:02
- IntelliJ IDEA as an IDE 00:01:58
- The Breeze Library for Linear Algebra 00:02:56
- WISP for Plotting 00:02:23
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Exploratory Data Analysis with Scala
- Exploratory Data Analysis 00:02:52
- Using DataFrames with Scala and Plotting with Breeze 00:04:33
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Supervised Learning
- Supervised Learning Problem Formulation 00:03:01
- Two Basic Regression Algorithms 00:04:26
- Implementing Linear Regression and GLMs in Scala 00:04:35
- Two Basic Classification Algorithms 00:04:30
- Implementing K-Nearest Neighbors and Naive Bayes in Scala 00:07:27
- Model Selection 00:05:32
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Unsupervised Learning
- Unsupervised Learning Problem Formulation 00:03:32
- Implementing K-means Algorithm in Scala 00:05:40
- Mixture of Gaussians Clustering 00:04:11
- Implementing Mixture of Gaussians Clustering in Scala 00:05:09
- Dimensionality Reduction with Principle Component Analysis (PCA) 00:03:30
- Implementing PCA in Scala 00:03:18
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Neural Networks
- Introduction to Feed-Forward Neural Networks 00:05:13
- Implementing the Feed-Forward Neural Network in Scala 00:05:01
- Introduction to Restricted Boltzmann Machines (RBMs) 00:04:14
- Implementing Restricted Boltzmann Machines in Scala 00:04:21
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Other Scala Frameworks for Machine Learning
- The Akka Actor Model for Concurrency 00:04:04
- A Multi-threaded K-Nearest Neighbors Implementation with Akka 00:06:39
- Introduction to Apache Spark 00:04:16
- Running Linear Regression on Spark with MLlib 00:03:46
- Certificate
Instructor : Packt
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With which software version is this tutorial compatible with?Scala
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What is the required level to follow this tutorial ?beginner
