Section 1 : Introduction and Housekeeping

 		Lecture 1 Downloadable Resources Text
Lecture 2 Introduction copy 3:3
Lecture 3 Increase the speed of learning Text
Lecture 4 Overview - AWS Machine Learning Specialty Exam 9:5
Lecture 5 Exam - Gap Analysis Pdf
Lecture 6 Preparation - AWS Machine Learning Specialty Exam 4:21
Lecture 7 AWS Account Setup, Free Tier Offers, Billing, Support 7:0
Lecture 8 Billing Alerts, Delegate Access 8:10
Lecture 9 Configure IAM Users, Setup Command Line Interface (CLI) 11:30
Lecture 10 Benefits of Cloud Computing 6:12
Lecture 11 AWS Global Infrastructure Overview 5:58
Lecture 12 Security is Job Zero AWS Public Sector Summit 2016 by Steve Schmidt Text

Section 2 : SageMaker Housekeeping

 		Lecture 13 Downloadable Resources Text
Lecture 14 Lab - S3 Bucket Setup 2:53
Lecture 15 Lab - Setup SageMaker Notebook Instance 2:49
Lecture 16 Lab - Source Code Setup 2:26
Lecture 17 Kaggle Data Setup Pdf
Lecture 18 SageMaker Console looks different from the course videos - Why Text
Lecture 19 How to download Kaggle data with code Pdf

Section 3 : Machine Learning Concepts

 		Lecture 20 Introduction to Machine Learning, Concepts, Terminologies 10:23
Lecture 21 Data Types - How to handle mixed data types 12:42
Lecture 22 Introduction to Python Notebook Environment 10:33
Lecture 23 Introduction to working with Missing Data 9:35
Lecture 24 Data Visualization - Linear, Log, Quadratic and More 4:38

Section 4 : Model Performance Evaluation

 		Lecture 25 Model Performance Text
Lecture 26 Downloadable Resources Pdf
Lecture 27 Introduction 3:26
Lecture 28 Regression Model Performance 9:58
Lecture 29 Binary Classifier Performance 8:0
Lecture 30 Binary Classifier - Confusion Matrix 6:56
Lecture 31 Binary Classifier - SKLearn Confusion Matrix 3:18
Lecture 32 Binary Classifier - Metrics Definition 3:52
Lecture 33 Binary Classifier - Metrics Calculation 4:26
Lecture 34 Question - Why not Model 1 Pdf
Lecture 35 Binary Classifier - Area Under Curve Metrics 9:40
Lecture 36 Multiclass Classifier 12:36
Lecture 37 Model Performance Text

Section 5 : SageMaker Service Overview

 		Lecture 38 Downloadable Resources Pdf
Lecture 39 Introduction to SageMaker 4:54
Lecture 40 Instance Type and Pricing 10:21
Lecture 41 DataFormat 11:12
Lecture 42 SageMaker Built-in Algorithms 9:36
Lecture 43 Popular Frameworks and Bring Your Own Algorithm 5:24

Section 6 : XGBoost - Gradient Boosted Trees

 		Lecture 44 Downloadable Resources Text
Lecture 45 Introduction to XGBoost 8:53
Lecture 46 Lab - Data Preparation Simple Regression
Lecture 47 Lab - Training Simple Regression 12:25
Lecture 48 Lab - Data Preparation Non-linear Data set 2:39
Lecture 49 Lab - Training Non-linear Data set 4:48
Lecture 50 Exercise - Improving quality of predictions Text
Lecture 51 Lab - Data Preparation Bike Rental Regression 8:24
Lecture 52 Lab - Train Bike Rental Regression Model 6:10
Lecture 53 Lab - Train using Log of Count 4:14
Lecture 54 ResourceLimitExceeded Error - How to Increase Resource Limit Text
Lecture 55 Use SageMaker SDK 2 Text
Lecture 56 Lab - How to train using SageMaker's built-in XGBoost Algorithm 7:36
Lecture 57 Q&A How does SageMaker built-in know the target variable Text
Lecture 58 Lab - How to run predictions against an existing SageMaker Endpoint 4:29
Lecture 59 Additional Integration Scenarios and Examples Text
Lecture 60 SageMaker Endpoint Features 5:41
Lecture 61 SageMaker Spot Instances - Save up to 90% for training jobs Pdf
Lecture 62 Lab - Multi-class Classification 5:41
Lecture 63 Lab - Binary Classification 6:22
Lecture 64 Exercise - Improve Data Quality in Diabetes dataset Text
Lecture 65 Question on Diabetes Data Quality Improvement Pdf
Lecture 66 Question on Diabetes model - is group mean on target the right approach Pdf
Lecture 67 HyperParameter Tuning, Bias-Variance, Regularization (L1, L2) 11:8
Lecture 68 Exercise - Mushroom Classification Pdf

Section 7 : Invoke Model Endpoint From External Clients

 		Lecture 69 Install SageMaker SDK, GIT Client, Source Code, Security Permissions Text
Lecture 70 IAM users for the lab Text
Lecture 71 Integration Overview 2:32
Lecture 72 Client to Endpoint using SageMaker SDK 9:26
Lecture 73 Client to Endpoint using Boto3 SDK 3:51
Lecture 74 Microservice - Lambda to Endpoint - Payload 3:24
Lecture 75 Lambda UI Changes Text
Lecture 76 Microservice - Lambda to Endpoint 9:10
Lecture 77 Microservice - API Gateway, Lambda to Endpoint 10:34

Section 8 : SageMaker - Principal Component Analysis (PCA)

 		Lecture 78 Normalization and Standardization Pdf
Lecture 79 Downloadable Resources Text
Lecture 80 Introduction to Principal Component Analysis (PCA) 5:49
Lecture 81 PCA Demo Overview 1:16
Lecture 82 Demo - PCA with Random Dataset 3:29
Lecture 83 Demo - PCA with Correlated Dataset 5:26
Lecture 84 Cleanup Resources on SageMaker Text
Lecture 85 Demo - PCA with Kaggle Bike Sharing - Overview and Normalization 3:52
Lecture 86 Demo - PCA Local Mode with Kaggle Bike Train 3:31
Lecture 87 Use SageMaker SDK 2 Pdf
Lecture 88 Demo - PCA training with SageMaker 4:23
Lecture 89 Demo - PCA Projection with SageMaker 2:42
Lecture 90 Exercise Kaggle Bike Train and PCA Text
Lecture 91 Summary 1:22

Section 9 : Recommender Systems - Factorization Machines

 		Lecture 92 Recommender System Text
Lecture 93 Downloadable Resources Text
Lecture 94 Introduction to Factorization Machines 5:59
Lecture 95 MovieLens Dataset Text
Lecture 96 Use SageMaker SDK 2 Pdf
Lecture 97 Demo - Movie Recommender Data Preparation 10:35
Lecture 98 Demo - Movie Recommender Model Training 5:35
Lecture 99 Demo - Movie Predictions By User 7:10

Section 10 : Model Optimization and HyperParameter Tuning

 		Lecture 100 Downloadable Resources Text
Lecture 101 Introduction to Hyperparameter Tuning 6:11
Lecture 102 Use SageMaker SDK 2 Pdf
Lecture 103 Lab Tuning Movie Rating Factorization Machine Recommender System 18:5
Lecture 104 Lab Step 2 Tuning Movie Rating Recommender System 5:1
Lecture 105 HyperParameter, Bias-Variance, Regularization (L1, L2) [Repeat from XGBoost] 11:8
Lecture 106 Nuts and Bolts of Optimization Pdf
Lecture 107 Model Optimization - related question Pdf

Section 11 : Time Series Forecasting - DeepAR

 		Lecture 108 Downloadable Resources Text
Lecture 109 Introduction to DeepAR Time Series Forecasting 9:47
Lecture 110 DeepAR Training and Inference Formats 9:49
Lecture 111 Working with Time Series Data, Handling Missing Values 9:59
Lecture 112 Use SageMaker SDK 2 Pdf
Lecture 113 Demo - Bike Rental as Time Series Forecasting Problem 11:44
Lecture 114 Demo - Bike Rental Model Training 7:21
Lecture 115 Demo - Bike Rental Prediction 4:50
Lecture 116 Demo - DeepAR Categories 6:10
Lecture 117 Demo - DeepAR Dynamic Features Data Preparation 6:34
Lecture 118 Demo - DeepAR Dynamic Features Training and Prediction
Lecture 119 Summary 1:16
Lecture 120 Question How to train a model for different products using DeepAR Pdf

Section 12 : Integration Options for Model Endpoint

 		Lecture 121 Lectures moved to Invoke Model Endpoint From External Clients section Text

Section 13 : Model Optimization and HyperParameter Tuning

 		Lecture 122 Lectures moved Text

Section 14 : Anomaly Detection - Random Cut Forest

 		Lecture 123 Downloadable Resources Text
Lecture 124 Introduction to Random Cut Forest and Intuition Behind Anomaly Detection
Lecture 125 Use SageMaker SDK 2 Pdf
Lecture 126 Lab - Taxi Passenger Traffic Analysis (AWS Provided Example) 8:53
Lecture 127 Lab - Auto Sales Analysis 5:55

Section 15 : Artificial Intelligence (AI) Services

 		Lecture 128 Downloadable Resources Text
Lecture 129 Lab Instructions Text
Lecture 130 Introduction 3:15
Lecture 131 2.1 Amazon Transcribe and Lab 5:33
Lecture 132 2.2 Amazon Transcribe and Lab 6:35
Lecture 133 3. Amazon Translate 4:29
Lecture 134 Translate - Practical Scenario Text
Lecture 135 4.1 Amazon Comprehend 5:43
Lecture 136 Pricing Comprehend Pdf
Lecture 137 4.2 Amazon Comprehend 5:0
Lecture 138 4.3 Amazon Comprehend training 8:35
Lecture 139 5. Amazon Polly 4:16
Lecture 140 6. Amazon Lex Pdf
Lecture 141 7. Amazon Rekognition 8:21
Lecture 142 8. Amazon Textract & Summary 3:3

Section 16 : S3 Data Lake Architecture - Data Consolidation

 		Lecture 143 Downloadable Resources Text
Lecture 144 Lab Instructions Text
Lecture 145 Introduction to Data Lake 10:28
Lecture 146 Kinesis - Streaming and Batch Processing 5:24
Lecture 147 Data Formats and Tools for Data Format Conversion 8:34
Lecture 148 In-Place Analytics and Portfolio of Tools 5:2
Lecture 149 Monitoring and Optimization 6:27
Lecture 150 Security and Protection 6:37
Lecture 151 Lab – Glue Data Catalog 8:31
Lecture 152 Set up a query result location in S3 Pdf
Lecture 153 Lab - Query with Athena 2:1
Lecture 154 Lab - Glue ETL - Convert format to Parquet 4:43
Lecture 155 Lab - Query Amazon Customer Reviews with Athena 5:6
Lecture 156 Lab – Sentiment of the Customer Review 6:7
Lecture 157 Lab - Query Sentiment of Customer Reviews using Athena 4:17
Lecture 158 Lambda UI Changes Text
Lecture 159 Lab – Serverless Customer Review Solution Part 1 9:45
Lecture 160 Lab – Serverless Customer Review Solution Part 2 7:52

Section 17 : Deep Learning and Neural Networks

 		Lecture 161 ReadMe and Downloadable Resources Text
Lecture 162 Lab Instructions Text
Lecture 163 Concepts - Gradient Descent, Loss Function for Regression 14:12
Lecture 164 Concepts - Gradient Descent, Loss Function for Classification 10:2
Lecture 165 Neural Networks and Deep Learning 7:35
Lecture 166 Introduction to Deep Learning Pdf
Lecture 167 Convolutional Neural Network Text
Lecture 168 Recurrent Neural Networks (RNN), LSTM Text
Lecture 169 Generative Adversarial Networks (GANs) Text
Lecture 170 Nuts and Bolts of Optimization [Repeat] Pdf
Lecture 171 Use SageMaker SDK 2 Pdf
Lecture 172 Lab - Regression with SKLearn Neural Network 6:38
Lecture 173 Lab - Regression with Keras and TensorFlow 7:24
Lecture 174 Lab - Binary Classification - Part 1- Customer Churn Prediction 5:59
Lecture 175 Lab - Binary Classification - Part 2 - Customer Churn Prediction 7:32
Lecture 176 Lab - Multiclass Classification - Iris 4:48
Lecture 177 Transfer Learning Text
Lecture 178 Optimizing for GPUs Pdf
Lecture 179 Multi-Class Multi-Label Classification Text
Lecture 180 SageMaker local mode for popular Frameworks Pdf

Section 18 : Bring Your Own Algorithm

 		Lecture 181 Downloadable Resources Text
Lecture 182 Introduction and How built-in algorithms work 5:6
Lecture 183 Custom Image and Popular Framework 3:55
Lecture 184 Folder Structure and Environment Variables 7:19
Lecture 185 About Certification Pdf
Lecture 186 Lab - SKLearn Estimator Bring Your Own Part 1 9:22
Lecture 187 Lab - SKLearn Estimator Bring Your Own Part 2 8:15
Lecture 188 Lab - TensorFlow Estimator Bring Your Own 3:51

Section 19 : Storage for Servers

 		Lecture 189 Downloadable Resources Text
Lecture 190 Introduction to Storage 8:40
Lecture 191 Elastic Block Store (EBS) 13:9
Lecture 192 Elastic File System, FSx for Windows, FSx for Lustre 4:53
Lecture 193 Elastic Block Store (EBS) Encryption Pdf

Section 20 : AWS - Support Plans and Feedback

 		Lecture 194 AWS Product Improvement Feedback Pdf
Lecture 195 How to contact AWS for Production Support 7:14

Section 21 : Databases on AWS

 		Lecture 196 Downloadable Resources Text
Lecture 197 AWS Databases - Introduction, Benefits, and Types
Lecture 198 Relational Database Service (RDS) - Features and Benefits 12:41
Lecture 199 Aurora and Aurora Serverless Relational Database 4:47
Lecture 200 DynamoDB - Primary Key, Partitions, and Features 8:3
Lecture 201 Cassandra and DocumentDB 2:30
Lecture 202 Amazon ElastiCache - Usage Example, Features 5:30
Lecture 203 Amazon Redshift 2:26

Section 22 : On-Premises usage and other technologies

 		Lecture 204 On-Premises Usage and other technologies Pdf