Section 1 : Course Introduction
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 Course FAQs
Lecture 3 Scala and Spark Overview 00:10:35 Duration

Section 2 : Scala IDE Options and Overview
Lecture 1 ScalaIDE Overview 00:02:46 Duration
Lecture 2 Computer Set-up Time!

Section 3 : Windows Scala and Spark Set-up and Installation
Lecture 1 Windows Introduction 00:00:40 Duration
Lecture 2 Quick note about Windows Installation
Lecture 3 Windows Scala and Spark Installation 00:12:04 Duration
Lecture 4 Atom Windows Installation
Lecture 5 Terminal Exericse

Section 4 : Mac OS Setup and Installation
Lecture 1 Mac OS Installation and Setup 00:09:58 Duration

Section 5 : Linux (Ubuntu) Setup and Installation
Lecture 1 Installing Scala and Spark on Linux (Ubuntu) 00:12:44 Duration

Section 6 : Scala Programming Level One
Lecture 1 Arithmetic and Numbers
Lecture 2 Values and Variables 00:07:44 Duration
Lecture 3 Booleans and Comparison Operators 00:02:06 Duration
Lecture 4 Strings and Basic Regex 00:12:43 Duration
Lecture 5 Tuples 00:02:31 Duration
Lecture 6 Scala Basics - Assessment Test Exercises 00:00:32 Duration
Lecture 7 Scala Basics Assessment Test Questions
Lecture 8 Scala Basics - Assessment Test Solutions 00:05:48 Duration

Section 7 : Collections
Lecture 1 Intro to Collections 00:00:40 Duration
Lecture 2 Lists 00:08:22 Duration
Lecture 3 Arrays 00:03:43 Duration
Lecture 4 Sets
Lecture 5 Maps 00:07:13 Duration
Lecture 6 Collections - Assessment Test Exercise 00:00:24 Duration
Lecture 7 Scala Collections Assessment Test
Lecture 8 Collections Assessment Test - Solutions 00:06:09 Duration

Section 8 : Scala Programming Level Two
Lecture 1 Flow Control 00:08:29 Duration
Lecture 2 For Loops
Lecture 3 While Loops 00:05:49 Duration
Lecture 4 Functions 00:12:45 Duration
Lecture 5 Scala Programming Exercises 00:02:28 Duration
Lecture 6 Scala Programming Exercises - Solutions 00:15:20 Duration

Section 9 : Spark DataFrames with Scala
Lecture 1 Quick Note for Windows Users!
Lecture 2 Introduction to Spark DataFrames 00:06:23 Duration
Lecture 3 DataFrames Overview 00:18:06 Duration
Lecture 4 Spark DataFrame Operations 00:16:17 Duration
Lecture 5 GroupBy and Aggregate Functions 00:10:48 Duration
Lecture 6 Missing data 00:13:11 Duration
Lecture 7 Date and Timestamps 00:09:46 Duration
Lecture 8 Quick Note on DataFrame Project
Lecture 9 DataFrame Project Exercises 00:01:28 Duration
Lecture 10 DataFrame Project - Solutions 00:20:16 Duration

Section 10 : Introduction to Machine Learning
Lecture 1 Introduction to Machine Learning 00:06:45 Duration
Lecture 2 Machine Learning with Spark 00:11:45 Duration
Lecture 3 IntelliJ IDEA Installation Overview 00:11:03 Duration

Section 11 : Regression with Spark
Lecture 1 Introduction to Linear Regression 00:06:06 Duration
Lecture 2 Introduction to Regression Section 00:01:01 Duration
Lecture 3 Linear Regression Documentation Example 00:08:24 Duration
Lecture 4 Alternate Linear Regression Data CSV File
Lecture 5 Linear Regression Walkthrough Part 1 00:16:35 Duration
Lecture 6 Linear Regression Walkthrough Part 2 00:07:21 Duration
Lecture 7 Linear Regression Exercise Project 00:02:28 Duration
Lecture 8 Linear Regression Project Solutions 00:16:52 Duration

Section 12 : Classification with Spark
Lecture 1 Introduction to Classification 00:12:37 Duration
Lecture 2 Classification Documentation Example 00:07:34 Duration
Lecture 3 Spark Classification - Logistic Regression Example - Part 1 00:15:44 Duration
Lecture 4 Spark Classification - Logistic Regression Example - Part 2 00:21:35 Duration
Lecture 5 Logistic Regression Project Exercise 00:01:47 Duration
Lecture 6 Classification Project Solutions 00:15:11 Duration

Section 13 : Model Evaluation
Lecture 1 Model Evaluation Overview 00:10:17 Duration
Lecture 2 Spark Model Evaluation - Documentation Example 00:21:27 Duration
Lecture 3 Spark - Model Evaluation - Regression Example 00:23:11 Duration

Section 14 : Clustering with Spark
Lecture 1 Introduction to Clustering with Spark 00:01:32 Duration
Lecture 2 KMeans Theory Lecture 00:05:00 Duration
Lecture 3 Note on Kmeans
Lecture 4 Example of KMeans with Spark 00:07:10 Duration
Lecture 5 Clustering Project Exercise Overview 00:03:37 Duration
Lecture 6 Clustering Project Exercises - Solutions 00:10:26 Duration

Section 15 : PCA with Spark
Lecture 1 PCA Theory Overview
Lecture 2 PCA with Spark - Documentation Example 00:05:56 Duration
Lecture 3 PCA with Spark - Project Exercise 00:03:01 Duration
Lecture 4 PCA Spark Exercise - Solutions 00:10:35 Duration

Section 16 : DataBricks and Spark
Lecture 1 Databricks Overview 00:17:19 Duration
Lecture 2 Introduction to Spark Recommendation Systems 00:03:58 Duration
Lecture 3 Spark Recommender System Implementation 00:13:30 Duration
Lecture 4 Zeppelin Notebooks on AWS Elastic MapReduce 00:19:57 Duration
Lecture 5 So what's next