Learning R for Data Visualization
Learning R for Data Visualization
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.
What will you learn in this course?
Course plan
Chapter 1
Introduction to Scala
Introduction to Scala
Chapter 2
Exploratory Data Analysis with Scala
Exploratory Data Analysis with Scala
Chapter 3
Supervised Learning
Supervised Learning
Chapter 4
Unsupervised Learning
Unsupervised Learning
Chapter 5
Neural Networks
Neural Networks
Chapter 6
Other Scala Frameworks for Machine Learning
Other Scala Frameworks for Machine Learning
Detailed course plan
Chapter 1 : Introduction to Scala
18m21
Lesson 1The Course Overview
Lesson 2Functional Combinators in Scala
Lesson 3Scala Traits, Classes, and Objects
Lesson 4IntelliJ IDEA as an IDE
Lesson 5The Breeze Library for Linear Algebra
Lesson 6WISP for Plotting
Chapter 2 : Exploratory Data Analysis with Scala
07m25
Lesson 1Exploratory Data Analysis
Lesson 2Using DataFrames with Scala and Plotting with Breeze
Chapter 3 : Supervised Learning
29m31
Lesson 1Supervised Learning Problem Formulation
Lesson 2Two Basic Regression Algorithms
Lesson 3Implementing Linear Regression and GLMs in Scala
Lesson 4Two Basic Classification Algorithms
Lesson 5Implementing K-Nearest Neighbors and Naive Bayes in Scala
Lesson 6Model Selection
Chapter 4 : Unsupervised Learning
25m20
Lesson 1Unsupervised Learning Problem Formulation
Lesson 2Implementing K-means Algorithm in Scala
Lesson 3Mixture of Gaussians Clustering
Lesson 4Implementing Mixture of Gaussians Clustering in Scala
Lesson 5Dimensionality Reduction with Principle Component Analysis (PCA)
Lesson 6Implementing PCA in Scala
Chapter 5 : Neural Networks
18m49
Lesson 1Introduction to Feed-Forward Neural Networks
Lesson 2Implementing the Feed-Forward Neural Network in Scala
Lesson 3Introduction to Restricted Boltzmann Machines (RBMs)
Lesson 4Implementing Restricted Boltzmann Machines in Scala
Chapter 6 : Other Scala Frameworks for Machine Learning
18m45
Lesson 1The Akka Actor Model for Concurrency
Lesson 2A Multi-threaded K-Nearest Neighbors Implementation with Akka
Lesson 3Introduction to Apache Spark
Lesson 4Running Linear Regression on Spark with MLlib
