Section 1 : Introduction to Course

Lecture 1 Introduction 2:44
Lecture 2 Course Overview 7:49
Lecture 3 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 4 What is Spark Why Python 18:51

Section 2 : Setting up Python with Spark

Lecture 5 Set-up Overview 5:50
Lecture 6 Note on Installation Sections Text

Section 3 : Databricks Setup

Lecture 7 About Proctor Testing Pdf
Lecture 8 Databricks Setup 11:36

Section 4 : Local VirtualBox Set-up

Lecture 9 Local Installation VirtualBox Part 1 10:44
Lecture 10 Local Installation VirtualBox Part 2 13:59
Lecture 11 Setting up PySpark 5:40

Section 5 : AWS EC2 PySpark Set-up

Lecture 12 AWS EC2 Set-up Guide
Lecture 12 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 14 SSH with Mac or Linux 4:49
Lecture 15 About Proctor Testing Pdf

Section 6 : AWS EMR Cluster Setup

Lecture 16 AWS EMR Setup 17:7

Section 7 : Python Crash Course

Lecture 17 Introduction to Python Crash Course 1:26
Lecture 18 Jupyter Notebook Overview
Lecture 19 Python Crash Course Part One
Lecture 20 Python Crash Course Part Two 11:52
Lecture 21 Python Crash Course Part Three 10:59
Lecture 22 Python Crash Course Exercises 1:9
Lecture 23 Python Crash Course Exercise Solutions 9:20

Section 8 : Spark DataFrame Basics

Lecture 24 Introduction to Spark DataFrames 2:19
Lecture 25 Spark DataFrame Basics 10:35
Lecture 26 Spark DataFrame Basics Part Two 9:39
Lecture 27 Spark DataFrame Basic Operations 9:55
Lecture 28 Groupby and Aggregate Operations 11:39
Lecture 29 Missing Data 8:19
Lecture 30 Dates and Timestamps 9:44

Section 9 : Spark DataFrame Project Exercise

Lecture 31 DataFrame Project Exercise
Lecture 32 DataFrame Project Exercise Solutions 16:39

Section 10 : Introduction to Machine Learning with MLlib

Lecture 33 Introduction to Machine Learning and ISLR 10:14
Lecture 34 Machine Learning with Spark and Python with MLlib 8:57

Section 11 : Linear Regression

Lecture 35 Linear Regression Theory and Reading 4:53
Lecture 36 Linear Regression Documentation Example 14:12
Lecture 37 Regression Evaluation 6:40
Lecture 38 Linear Regression Example Code Along 15:7
Lecture 39 Linear Regression Consulting Project 3:4
Lecture 40 Linear Regression Consulting Project Solutions 15:3

Section 12 : Logistic Regression

Lecture 41 Logistic Regression Theory and Reading 11:9
Lecture 42 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 43 About Proctor Testing Pdf
Lecture 44 Logistic Regression Consulting Project 3:6
Lecture 45 Logistic Regression Consulting Project Solutions 10:59

Section 13 : Decision Trees and Random Forests

Lecture 46 About Certification Pdf
Lecture 47 Tree Methods Documentation Examples 13:9
Lecture 48 Decision Tress and Random Forest Code Along Examples 20:29
Lecture 49 Random Forest - Classification Consulting Project 2:26
Lecture 50 Random Forest Classification Consulting Project Solutions

Section 14 : K-means Clustering

Lecture 51 K-means Clustering Theory and Reading 6:47
Lecture 52 KMeans Clustering Documentation Example 9:43
Lecture 53 Clustering Example Code Along 12:38
Lecture 54 Clustering Consulting Project 3:2
Lecture 55 Clustering Consulting Project Solutions 8:29

Section 15 : Collaborative Filtering for Recommender Systems

Lecture 56 Introduction to Recommender Systems 6:26
Lecture 57 Recommender System - Code Along Project 11:30

Section 16 : Natural Language Processing

Lecture 58 Introduction to Natural Language Processing 7:54
Lecture 59 NLP Tools Part One 15:31
Lecture 60 NLP Tools Part Two 7:40
Lecture 61 Natural Language Processing Code Along Project 13:57

Section 17 : Spark Streaming with Python

Lecture 62 Introduction to Streaming with Spark! 10:11
Lecture 63 Spark Streaming Documentation Example 11:48
Lecture 64 Spark Streaming Twitter Project - Part 4:23
Lecture 65 Spark Streaming Twitter Project - Part Two 12:38
Lecture 66 Spark Streaming Twitter Project - Part Three 17:28