Section 1 : Course Introduction

Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 Course FAQs Text
Lecture 3 Scala and Spark Overview 10:35

Section 2 : Scala IDE Options and Overview

Lecture 4 ScalaIDE Overview 2:46
Lecture 5 Computer Set-up Time! Text

Section 3 : Windows Scala and Spark Set-up and Installation

Lecture 6 Windows Introduction 0:40
Lecture 7 Quick note about Windows Installation Text
Lecture 8 Windows Scala and Spark Installation 12:4
Lecture 9 Atom Windows Installation
Lecture 10 Terminal Exericse Text

Section 4 : Mac OS Setup and Installation

Lecture 11 Mac OS Installation and Setup 9:58

Section 5 : Linux (Ubuntu) Setup and Installation

Lecture 12 Installing Scala and Spark on Linux (Ubuntu) 12:44

Section 6 : Scala Programming Level One

Lecture 13 Arithmetic and Numbers
Lecture 14 Values and Variables 7:44
Lecture 15 Booleans and Comparison Operators 2:6
Lecture 16 Strings and Basic Regex 12:43
Lecture 17 Tuples 2:31
Lecture 18 Scala Basics - Assessment Test Exercises 0:32
Lecture 19 Scala Basics Assessment Test Questions Text
Lecture 20 Scala Basics - Assessment Test Solutions 5:48

Section 7 : Collections

Lecture 21 Intro to Collections 0:40
Lecture 22 Lists 8:22
Lecture 23 Arrays 3:43
Lecture 24 Sets
Lecture 25 Maps 7:13
Lecture 26 Collections - Assessment Test Exercise 0:24
Lecture 27 Scala Collections Assessment Test Text
Lecture 28 Collections Assessment Test - Solutions 6:9

Section 8 : Scala Programming Level Two

Lecture 29 Flow Control 8:29
Lecture 30 For Loops
Lecture 31 While Loops 5:49
Lecture 32 Functions 12:45
Lecture 33 Scala Programming Exercises 2:28
Lecture 34 Scala Programming Exercises - Solutions 15:20

Section 9 : Spark DataFrames with Scala

Lecture 35 Quick Note for Windows Users! Text
Lecture 36 Introduction to Spark DataFrames 6:23
Lecture 37 DataFrames Overview 18:6
Lecture 38 Spark DataFrame Operations 16:17
Lecture 39 GroupBy and Aggregate Functions 10:48
Lecture 40 Missing data 13:11
Lecture 41 Date and Timestamps 9:46
Lecture 42 Quick Note on DataFrame Project Text
Lecture 43 DataFrame Project Exercises 1:28
Lecture 44 DataFrame Project - Solutions 20:16

Section 10 : Introduction to Machine Learning

Lecture 45 Introduction to Machine Learning 6:45
Lecture 46 Machine Learning with Spark 11:45
Lecture 47 IntelliJ IDEA Installation Overview 11:3

Section 11 : Regression with Spark

Lecture 48 Introduction to Linear Regression 6:6
Lecture 49 Introduction to Regression Section 1:1
Lecture 50 Linear Regression Documentation Example 8:24
Lecture 51 Alternate Linear Regression Data CSV File Text
Lecture 52 Linear Regression Walkthrough Part 1 16:35
Lecture 53 Linear Regression Walkthrough Part 2 7:21
Lecture 54 Linear Regression Exercise Project 2:28
Lecture 55 Linear Regression Project Solutions 16:52

Section 12 : Classification with Spark

Lecture 56 Introduction to Classification 12:37
Lecture 57 Classification Documentation Example 7:34
Lecture 58 Spark Classification - Logistic Regression Example - Part 1 15:44
Lecture 59 Spark Classification - Logistic Regression Example - Part 2 21:35
Lecture 60 Logistic Regression Project Exercise 1:47
Lecture 61 Classification Project Solutions 15:11

Section 13 : Model Evaluation

Lecture 62 Model Evaluation Overview 10:17
Lecture 63 Spark Model Evaluation - Documentation Example 21:27
Lecture 64 Spark - Model Evaluation - Regression Example 23:11

Section 14 : Clustering with Spark

Lecture 65 Introduction to Clustering with Spark 1:32
Lecture 66 KMeans Theory Lecture 5:0
Lecture 67 Note on Kmeans Text
Lecture 68 Example of KMeans with Spark 7:10
Lecture 69 Clustering Project Exercise Overview 3:37
Lecture 70 Clustering Project Exercises - Solutions 10:26

Section 15 : PCA with Spark

Lecture 71 PCA Theory Overview
Lecture 72 PCA with Spark - Documentation Example 5:56
Lecture 73 PCA with Spark - Project Exercise 3:1
Lecture 74 PCA Spark Exercise - Solutions 10:35

Section 16 : DataBricks and Spark

Lecture 75 Databricks Overview 17:19
Lecture 76 Introduction to Spark Recommendation Systems 3:58
Lecture 77 Spark Recommender System Implementation 13:30
Lecture 78 Zeppelin Notebooks on AWS Elastic MapReduce 19:57
Lecture 79 So what's next Text