Books & Videos

Table of Contents

Chapter: Introduction to Scala

The Course Overview

02m 28s

Functional Combinators in Scala

04m 33s

Scala Traits, Classes, and Objects

04m 1s

IntelliJ IDEA as an IDE

01m 57s

The Breeze Library for Linear Algebra

02m 55s

WISP for Plotting

02m 23s

Chapter: Exploratory Data Analysis with Scala

Exploratory Data Analysis

02m 51s

Using DataFrames with Scala and Plotting with Breeze

04m 32s

Chapter: Supervised Learning

Supervised Learning Problem Formulation

03m 0s

Two Basic Regression Algorithms

04m 26s

Implementing Linear Regression and GLMs in Scala

04m 34s

Two Basic Classification Algorithms

04m 30s

Implementing K-Nearest Neighbors and Naive Bayes in Scala

07m 27s

Model Selection

05m 32s

Chapter: Unsupervised Learning

Unsupervised Learning Problem Formulation

03m 32s

Implementing K-means Algorithm in Scala

05m 40s

Mixture of Gaussians Clustering

04m 10s

Implementing Mixture of Gaussians Clustering in Scala

05m 8s

Dimensionality Reduction with Principle Component Analysis (PCA)

03m 30s

Implementing PCA in Scala

03m 18s

Chapter: Neural Networks

Introduction to Feed-Forward Neural Networks

05m 12s

Implementing the Feed-Forward Neural Network in Scala

05m 1s

Introduction to Restricted Boltzmann Machines (RBMs)

04m 13s

Implementing Restricted Boltzmann Machines in Scala

04m 21s

Chapter: Other Scala Frameworks for Machine Learning

The Akka Actor Model for Concurrency

04m 4s

A Multi-threaded K-Nearest Neighbors Implementation with Akka

06m 39s

Introduction to Apache Spark

04m 16s

Running Linear Regression on Spark with MLlib

03m 45s