Contents

• Lecture 1 Chapter 1

  • Introduction 07:19
  • The Structure of the AWS Certified Machine Learning - Specialty Exam 11:32
  • Question Types and Real Exam Questions 09:48

• Lecture 2 Chapter 2

  • Introduction to Data Engineering 05:32
  • Identifying Data Sources 06:57
  • Data Storage Mediums - part 1 11:29
  • Data Storage Mediums - part 2 14:15
  • Data Job Styles and Types 07:44
  • Orchestrating Data Ingestion Pipelines 05:23
  • Data Ingestion with Amazon Kenesis 09:01
  • Data Ingestion with Amazon Glue 08:24
  • Data Ingestion with Amazon EMR 08:25
  • Data Ingestion with AWS Managed Service for Apache Flink 07:17
  • Handle ML Specific Data by Using MapReduce, Hadoop, Spark and Hive 08:57

• Lecture 3 Chapter 3

  • Introduction to Exploratory Data Analysis (EDA) 06:43
  • Identify and Handle Missing Data, Corrupt Data, and Stop Words 07:42
  • Formatting, Normalizing, and Scaling Data 08:03
  • Augmenting Data and Ensuring Sufficient Labeled Data 08:37
  • Feature Identification and Extraction 08:06
  • Evaluating Feature Engineering Techniques 07:57
  • Applying Feature Engineering Techniques 09:47
  • Interpreting Descriptive Statistics 09:25
  • Perform Cluster Analysis 08:37

• Lecture 4 Chapter 4

  • Introduction to Modeling 07:21
  • When to Use and Not Use Machine Learning 05:26
  • Supervised vs Unsupervised Learning 07:16
  • Selecting ML Problem Types 10:04
  • Understanding Model Intuition 10:18
  • Overview of Classification Models 11:40
  • Overview of Regression Models 10:14
  • Overview of Clustering and Recommendation Models 08:59
  • Deep Learning Models and Transfer Learning 11:23
  • Introduction to Large Language Models (LLMs) 07:28
  • Data Splitting Techniques 08:06
  • Optimization Techniques for ML Training 09:44
  • Batch vs Real-Time Model Training 07:37
  • Selecing Compute Resources and Platforms 09:20
  • Update and Retrain Models 06:54
  • Hyperparameter Optimization and Regularization 08:09
  • Neural Network, Tree-Based, and Linear Model Parameters 08:19
  • Avoiding Overfitting and Underfitting 09:35
  • Model Evaluation Metrics and Techniques 08:41
  • Cross-Validation and Advanced Evaluation 06:59

• Lecture 5 Chapter 5

  • Introduction to ML Implementation and Operation 06:51
  • Deploying ML Solutions to Multiple Regions and AZs 07:02
  • Scaling ML Solutions with Auto Scaling and Load Balancing 08:45
  • Optimizing ML Resources 08:05
  • Logging and Monitoring ML Solutions 08:02
  • Debugging and Troubleshooting 09:16
  • Introduction to AWS ML Services 10:01
  • Using Spot Instances and Cost Optimization 07:53
  • Securing Data with S3 Bucket Policies and Encryption 08:39
  • VPC and Security Groups 08:21
  • Deploying and Exposing Endpoints 07:57
  • Monitoring ML Models with SageMaker Model Monitor 08:48
  • Automating Model Retraining with SageMaker Pipelines 08:34

• Lecture 6 Chapter 6

  • Exam Preparation and Study Tips 06:23
  • Final Tips for Exam Day 06:24