Section 1 : Introduction

 		Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 Course Introduction What to Expect copy 6:10
Lecture 3 Get the Course Materials 1:42

Section 2 : Data Engineering

 		Lecture 4 Section Intro Data Engineering 1:5
Lecture 5 Amazon S3 - Overview 5:4
Lecture 6 Amazon S3 - Storage Tiers & Lifecycle Rules 4:30
Lecture 7 Amazon S3 Security 8:6
Lecture 8 Kinesis Data Streams & Kinesis Data Firehose 8:38
Lecture 9 Lab 1 6:4
Lecture 10 Kinesis Data Analytics 4:25
Lecture 11 Lab 1 7:23
Lecture 12 Kinesis Video Streams 2:55
Lecture 13 Kinesis ML Summary 1:12
Lecture 14 Glue Data Catalog & Crawlers 2:32
Lecture 15 Lab 1 4:23
Lecture 16 Glue ETL 2:10
Lecture 17 Lab 1 6:21
Lecture 18 Lab 1 1:27
Lecture 19 Lab 1 - Cleanup 1:32
Lecture 20 AWS Data Stores in Machine Learning 3:10
Lecture 21 AWS Data Pipelines 2:39
Lecture 22 AWS Batch 1:52
Lecture 24 AWS Step Functions 2:44
Lecture 25 Full Data Engineering Pipelines 5:9
Lecture 26 Data Engineering Summary Text

Section 3 : Exploratory Data Analysis

 		Lecture 27 Section Intro Data Analysis 1:13
Lecture 28 Python in Data Science and Machine Learning 12:8
Lecture 29 Example Preparing Data for Machine Learning in a Jupyter Notebook 10:21
Lecture 30 Types of Data 4:31
Lecture 31 Data Distributions 6:6
Lecture 32 Time Series Trends and Seasonality 3:57
Lecture 33 Introduction to Amazon Athena 5:7
Lecture 34 Overview of Amazon Quicksight 5:59
Lecture 35 Types of Visualizations, and When to Use Them
Lecture 36 Elastic MapReduce (EMR) and Hadoop Overview 7:15
Lecture 37 Apache Spark on EMR 9:59
Lecture 38 EMR Notebooks, Security, and Instance Types 4:10
Lecture 39 Feature Engineering and the Curse of Dimensionality 6:34
Lecture 40 Imputing Missing Data
Lecture 41 Dealing with Unbalanced Data 5:35
Lecture 42 Handling Outliers 8:30
Lecture 43 Binning, Transforming, Encoding, Scaling, and Shuffling 7:59
Lecture 44 Amazon SageMaker Ground Truth and Label Generation 4:28
Lecture 45 Lab Preparing Data for TF-IDF with Spark and EMR, Part 1 6:18
Lecture 46 Lab Preparing Data for TF-IDF with Spark and EMR, Part 2 9:46
Lecture 47 Lab Preparing Data for TF-IDF with Spark and EMR, Part 3 13:29

Section 4 : Modeling

 		Lecture 48 Section Intro Modeling 1:48
Lecture 49 Introduction to Deep Learning 9:23
Lecture 50 Activation Functions 10:50
Lecture 51 Convolutional Neural Networks 12:7
Lecture 52 Recurrent Neural Networks 10:49
Lecture 53 Deep Learning on EC2 and EMR 1:32
Lecture 54 Tuning Neural Networks 4:48
Lecture 55 Regularization Techniques for Neural Networks (Dropout, Early Stopping)
Lecture 56 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 57 L1 and L2 Regularization 3:4
Lecture 58 The Confusion Matrix 5:30
Lecture 59 Precision, Recall, F1, AUC, and more 6:59
Lecture 60 Ensemble Methods Bagging and Boosting 3:43
Lecture 61 Introducing Amazon SageMaker 8:7
Lecture 62 Linear Learner in SageMaker 4:59
Lecture 63 XGBoost in SageMaker 3:0
Lecture 64 Seq2Seq in SageMaker 4:47
Lecture 65 DeepAR in SageMaker
Lecture 66 BlazingText in SageMaker 4:56
Lecture 67 Object2Vec in SageMaker 4:44
Lecture 68 Object Detection in SageMaker 4:2
Lecture 69 Image Classification in SageMaker 4:8
Lecture 70 Semantic Segmentation in SageMaker 3:48
Lecture 71 Random Cut Forest in SageMaker 3:1
Lecture 72 Neural Topic Model in SageMaker 3:25
Lecture 73 Latent Dirichlet Allocation (LDA) in SageMaker 3:10
Lecture 74 K-Nearest-Neighbors (KNN) in SageMaker 3:0
Lecture 75 K-Means Clustering in SageMaker 5:0
Lecture 76 Principal Component Analysis (PCA) in SageMaker
Lecture 77 Factorization Machines in SageMaker 4:12
Lecture 78 IP Insights in SageMaker 2:58
Lecture 79 Reinforcement Learning in SageMaker 12:23
Lecture 80 Automatic Model Tuning 5:55
Lecture 81 Apache Spark with SageMaker 3:17
Lecture 82 SageMaker Studio, and new SageMaker features for 2020 6:6
Lecture 83 Amazon Comprehend 5:49
Lecture 84 Amazon Translate 1:55
Lecture 85 Amazon Transcribe 4:17
Lecture 86 Amazon Polly 5:38
Lecture 87 Amazon Rekognition 7:45
Lecture 88 Amazon Forecast 1:45
Lecture 89 Amazon Lex 3:7
Lecture 90 The Best of the Rest Other High-Level AWS Machine Learning Services 2:50
Lecture 91 New ML Services for 2020 6:19
Lecture 92 Putting them All Together 2:8
Lecture 93 Lab Tuning a Convolutional Neural Network on EC2, Part 1 8:59
Lecture 94 Lab Tuning a Convolutional Neural Network on EC2, Part 2 9:6
Lecture 95 Lab Tuning a Convolutional Neural Network on EC2, Part 3 6:29

Section 5 : ML Implementation and Operations

 		Lecture 96 Section Intro Machine Learning Implementation and Operations 1:10
Lecture 97 SageMaker's Inner Details and Production Variants 10:56
Lecture 98 SageMaker On the Edge SageMaker Neo and IoT Greengrass 4:18
Lecture 99 SageMaker Security Encryption at Rest and In Transit 4:31
Lecture 100 SageMaker Security VPC's, IAM, Logging, and Monitoring 4:3
Lecture 101 SageMaker Resource Management Instance Types and Spot Training 3:35
Lecture 102 SageMaker Resource Management Elastic Inference, Automatic Scaling, AZ's 4:34
Lecture 103 SageMaker Inference Pipelines 1:39
Lecture 104 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1 5:21
Lecture 105 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2 10:33
Lecture 106 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3 12:21

Section 6 : Wrapping Up

 		Lecture 107 Section Intro Wrapping Up 0:24
Lecture 108 More Preparation Resources 5:53
Lecture 109 Test-Taking Strategies, and What to Expect 10:4
Lecture 110 You Made It! 0:46
Lecture 111 Save 50% on your AWS Exam Cost! 1:42
Lecture 112 Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only 1:9