Section 1 : INTRODUCTION, DATAML LINGO, AWS DATA STORAGE

 		Lecture 1 What makes this course unique 4:57
Lecture 2 AWS Machine Learning Exam Overview 9:6
Lecture 3 Course Outline 10:13
Lecture 4 BONUS Learning Path Text
Lecture 5 Guidelines and Best Practices 1:56
Lecture 6 Section Introduction
Lecture 7 What is Machine Learning and AI - Part 1 9:49
Lecture 8 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 9 Amazon Web Services 6:24
Lecture 10 AIML Data Lingo - Labeled vs 14:4
Lecture 11 AIML Data Lingo - Data Types 10:4
Lecture 12 Database vs
Lecture 13 AWS Storage S3 DynamoDB RDS 11:2
Lecture 14 GET YOUR BONUS MATERIALS Text
Lecture 15 Section 1 Slides Text

Section 2 : AMAZON S3

 		Lecture 16 Section Introduction 2:40
Lecture 17 Amazon S3 Partitions and Tags 13:44
Lecture 18 S3 Storage Tiers and LifeCycle Polices 7:48
Lecture 19 S3 Encryption 5:5
Lecture 20 S3 Security - Part 1 9:1
Lecture 21 S3 Security - Part 2 5:12
Lecture 22 Additional Information 3:23
Lecture 23 GET YOUR BONUS MATERIALS Text
Lecture 24 Section 2 Slides Text

Section 3 : AWS DATA MIGRATION, GLUE, PIPELINE, STEP and BATCH

 		Lecture 25 Section Introduction 3:18
Lecture 26 AWS Glue – part #1
Lecture 27 AWS Glue – part #2 8:40
Lecture 28 AWS Data Pipeline 7:34
Lecture 29 AWS Data Migration Service DMS 3:33
Lecture 30 AWS Batch 2:57
Lecture 31 AWS Step Function 8:31
Lecture 32 GET YOUR BONUS MATERIALS Text
Lecture 33 Section 3 Slides Text

Section 4 : DATA STREAMING AND KINESIS

 		Lecture 34 Section Introduction 5:6
Lecture 35 Kinesis Overview 7:57
Lecture 36 AWS Kinesis Video Streams - Part 1 5:43
Lecture 37 AWS Kinesis Video Streams - Part 2 7:43
Lecture 38 AWS Kinesis Data Streams - Part 1 5:21
Lecture 39 AWS Kinesis Data Streams - Part 2 6:57
Lecture 40 AWS Kinesis Firehose 6:5
Lecture 41 AWS Kinesis Analytics - Part 1 3:20
Lecture 42 AWS Kinesis Analytics - Part 2 7:54
Lecture 43 GET YOUR BONUS MATERIALS Text
Lecture 44 Section 4 Slides Text

Section 5 : JUPYTER NOTEBOOK, SCIKIT-LEARN, PYTHON PACKAGES, AND DISTRIBUTIONS

 		Lecture 45 Section Introduction 6:42
Lecture 46 Jupyter Notebooks and Scikit Learn 6:33
Lecture 47 Python Packages (Pandas, Numpy, Matplotlib and Seaborn)
Lecture 48 Data Visualization 7:9
Lecture 49 Distributions (Normal, Standard, Poisson, Bernoulli) 10:12
Lecture 50 Time Series 2:35
Lecture 51 GET YOUR BONUS MATERIALS Text
Lecture 52 Section 5 Slides Text

Section 6 : ATHENA, QUICKSIGHT, EMR

 		Lecture 53 Section Introduction 3:13
Lecture 54 Athena - Part 1 8:55
Lecture 55 Athena - Part 2 7:55
Lecture 56 Amazon Quicksight - Part 1 4:56
Lecture 57 Amazon Quicksight - Part 2 11:27
Lecture 58 Elastic Map Reduce - Part 1 10:3
Lecture 59 Elastic Map Reduce - Part 2 11:21
Lecture 60 EMR and Hadoop 7:20
Lecture 61 EMR and Spark 5:34
Lecture 62 GET YOUR BONUS MATERIALS Text
Lecture 63 Section 6 Slides Text

Section 7 : FEATURE ENGINEERING

 		Lecture 64 Introduction to Feature Engineering 2:31
Lecture 65 Feature Engineering Overview 8:37
Lecture 66 Amazon SageMaker GroundTruth 9:7
Lecture 67 Feature Selection 5:25
Lecture 68 Scaling 9:28
Lecture 69 Imputation 10:21
Lecture 70 Outliers 5:8
Lecture 71 One Hot Encoding 3:42
Lecture 72 Binning 5:27
Lecture 73 Log Transformation 3:32
Lecture 74 Shuffling, Feature Splitting, Unbalanced Datasets 6:25
Lecture 75 Text Feature Engineering overview 3:40
Lecture 76 Bag of words, punctuation, and dates (easy ones!) 4:47
Lecture 77 Term Frequency Inverse Document Frequency (TF-IDF) 6:44
Lecture 78 N-Grams (Unigram vs 5:37
Lecture 79 Orthogonal Sparse Bigram (OSB) 3:14
Lecture 80 Cartesian Product Transformation 3:36
Lecture 81 GET YOUR BONUS MATERIALS Text
Lecture 82 Section 7 Slides Text

Section 8 : MACHINE AND DEEP LEARNING BASICS - PART #1

 		Lecture 83 Section Introduction 8:53
Lecture 84 Artificial Neural Networks Basics Single Neuron Model 7:2
Lecture 85 Activation Functions 5:1
Lecture 86 Multi-Layer Perceptron Model 7:31
Lecture 87 How do Artificial Neural Networks Train 16:24
Lecture 88 ANN Parameters Tuning – Learning rate and batch size 10:49
Lecture 89 Tensorflow playground 14:40
Lecture 90 Gradient Descent and Backpropagation 9:3
Lecture 91 Overfitting and Under fitting 6:9
Lecture 92 How to overcome overfitting 9:28
Lecture 93 Bias Variance Trade-off 9:51
Lecture 94 L2 Regularization 7:48
Lecture 95 L1 Regularization 4:33
Lecture 96 GET YOUR BONUS MATERIALS Text
Lecture 97 Section 8 Slides Text

Section 9 : MACHINE AND DEEP LEARNING BASICS - PART #2

 		Lecture 98 Section Introduction 2:39
Lecture 99 Artificial Neural Networks Architectures
Lecture 100 Convolutional Neural Networks 18:54
Lecture 101 Recurrent Neural Networks 9:51
Lecture 102 Vanishing Gradient Problem 7:16
Lecture 103 Long Short Term Memory (LSTM) Networks 7:10
Lecture 104 Model Performance Assessment – Confusion Matrix 7:55
Lecture 105 Model Performance Assessment – Precision, recall, F1-score 16:43
Lecture 106 Model Performance Assessment – ROC, AUC, Heatmap, and RMSE 9:35
Lecture 107 Transfer Learning 11:36
Lecture 108 Ensemble Learning - Bagging and Boosting 9:57
Lecture 109 K Fold Cross Validation 2:15
Lecture 110 GET YOUR BONUS MATERIALS Text
Lecture 111 Section 9 Slides Text

Section 10 : MACHINE AND DEEP LEARNING IN AWS - PART #1

 		Lecture 112 Section Introduction 3:46
Lecture 113 AWS SageMaker 11:7
Lecture 114 AWS SageMaker Part 2 12:11
Lecture 115 Deep Learning on AWS 2:44
Lecture 116 SageMaker Built-in algorithms overview 6:36
Lecture 117 Object Detection 9:9
Lecture 118 Image classification 6:28
Lecture 119 Semantic Segmentation 8:7
Lecture 120 Linear Learner 6:58
Lecture 121 Factorization Machines 4:32
Lecture 122 XGboost 3:34
Lecture 123 Seq2Seq 5:55
Lecture 124 DeepAR 8:15
Lecture 125 Blazing Text 9:52
Lecture 126 GET YOUR BONUS MATERIALS Text
Lecture 127 Section 10 Slides Text

Section 11 : MACHINE AND DEEP LEARNING IN AWS - PART #2

 		Lecture 128 Section Introduction 3:11
Lecture 129 SageMaker Built-in Algorithms Overview 4:34
Lecture 130 Random Cut Forest 7:45
Lecture 131 K Nearest Neighbors KNN 9:9
Lecture 132 K Means 4:35
Lecture 133 Principal Component Analysis PCA 3:51
Lecture 134 IP Insights 5:43
Lecture 135 Reinforcement Learning 9:26
Lecture 136 Neural Topic Model NTM 3:24
Lecture 137 LDA 3:37
Lecture 138 Object2Vec 6:23
Lecture 139 Multi Model 1:55
Lecture 140 Automatic Model Tuning 8:40
Lecture 141 GET YOUR BONUS MATERIALS Text
Lecture 142 Section 11 Slides Text

Section 12 : AWS HIGH LEVEL AIML SERVICES

 		Lecture 143 Section Introduction 3:47
Lecture 144 SageMaker AIML High Level Services 2:42
Lecture 145 Top 5 AIML Services 15:5
Lecture 146 ReKognition 5:54
Lecture 147 Amazon Comprehend and Comprehend Medical 5:43
Lecture 148 Translate 5:46
Lecture 149 Transcribe 6:25
Lecture 150 Polly 2:4
Lecture 151 Forecast 5:45
Lecture 152 Lex 5:0
Lecture 153 Personalize 3:41
Lecture 154 Textract 2:31
Lecture 155 AWS DeepLens 4:12
Lecture 156 AWS DeepRacer 2:38
Lecture 157 GET YOUR BONUS MATERIALS Text
Lecture 158 Section 12 Slides Text

Section 13 : ML IMPLEMENTATION AND OPERATION

 		Lecture 159 Introduction 6:57
Lecture 160 SageMaker Components Review 8:15
Lecture 161 SageMaker Model Deployment 5:16
Lecture 162 Resources and Instance Types 13:4
Lecture 163 Online vs 9:9
Lecture 164 Production Variants and Canary Deployment 4:58
Lecture 165 SageMaker Neo 5:9
Lecture 166 AWS IoT Greengrass 3:40
Lecture 167 Docker Containers 7:28
Lecture 168 AWS Security Overview 5:53
Lecture 169 In-Transit and Rest Encryption 3:41
Lecture 170 AWS CloudWatch 4:15
Lecture 171 AWS CloudTrail 2:55
Lecture 172 Section 13 Slides Text