Theory | Hands-On Labs | Practice Questions | Downloadable PDF Slides | Pass the certification exam | Latest Syllabus

What you'll learn

Design and implement scalable ML data pipelines using AWS services like Kinesis, Glue, EMR, and Firehose for batch and streaming workloads

Build, train, and optimize ML models using SageMaker with proper hyperparameter tuning, cross-validation, and evaluation metrics

Deploy production ML solutions with AWS security best practices including IAM policies, VPC configuration, and data encryption

Operationalize ML systems with monitoring, A/B testing, automated retraining pipelines, and performance optimization on AWS

Requirements

Basic understanding of machine learning concepts and Python programming is helpful, with AWS Machine Learning Associate certification providing a strong foundation but not required. You'll need an AWS free tier account for hands-on labs - all AWS services are explained from scratch with step-by-step demonstrations.

Description

Master AWS Machine Learning Services and Pass the MLS-C01 Certification ExamThis comprehensive course prepares you for the AWS Certified Machine Learning - Specialty (MLS-C01) certification exam through extensive content covering all essential AWS services. Aligned with the official AWS exam guide, the curriculum addresses all four certification domains: Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation & Operations (20%).What You'll Learn:Through structured lectures combining theory and hands-on demonstrations, you'll master the complete ML lifecycle on AWS. Build data ingestion pipelines using Kinesis, Glue, and EMR. Design feature engineering solutions with proper data preprocessing and transformation techniques. Train and optimize models using SageMaker's built-in algorithms and custom implementations. Deploy production-ready ML solutions with comprehensive security, monitoring, and operational best practices.Course Structure:Each AWS service receives dedicated coverage with theoretical explanation followed by practical demonstration. Starting with foundational analytics services (Athena, Redshift, QuickSight), you'll progress through compute options (EC2, Lambda, Batch), containerization (ECS, EKS, Fargate), and dive deep into ML-specific services. The centerpiece SageMaker section covers end-to-end model development, while AI services like Comprehend, Rekognition, Transcribe, and Textract demonstrate pre-built ML capabilities.Hands-On Learning:A significant portion of the course consists of hands-on labs where you'll implement real solutions in the AWS console. Configure VPCs for secure ML environments, set up IAM policies for least-privilege access, implement CloudWatch monitoring for model performance, and build complete ML pipelines from data ingestion to model deployment. Every demonstration uses free-tier eligible services, ensuring you can follow along without significant costs.Exam Preparation:Beyond service knowledge, you'll understand key ML concepts required for certification: hyperparameter optimization, cross-validation, bias-variance tradeoffs, evaluation metrics (AUC-ROC, F1, precision/recall), and model selection criteria. Learn when to use built-in algorithms versus custom models, how to right-size infrastructure for cost optimization, and best practices for MLOps including A/B testing and automated retraining pipelines.Additional Resources:The course includes downloadable PDF slides for offline review, practice questions aligned with exam format, and reference materials for continued learning. Each section builds upon previous knowledge, creating a structured learning path from fundamentals to advanced implementations.Who Should Enroll:Perfect for data scientists, ML engineers, cloud architects, and developers pursuing AWS ML specialty certification. Whether advancing from AWS ML Associate certification or building on existing ML experience, this course provides both theoretical knowledge and practical skills needed for exam success and real-world implementation.Start your journey to becoming an AWS Certified Machine Learning Specialist today.

Overview

Section 1: Introduction

Lecture 1 About The Instructor

Lecture 2 Exam Strategy, Tips & Tricks

Section 2: Analytics

Lecture 3 Amazon Athena

Lecture 4 Amazon Athena - Hands-On Demo

Lecture 5 Amazon Data Firehose

Lecture 6 Amazon EMR

Lecture 7 Amazon EMR - Hands-On Demo

Lecture 8 AWS Glue

Lecture 9 AWS Glue - Hands-On Demo

Lecture 10 Amazon Kinesis Data Streams

Lecture 11 Amazon Kinesis Data Streams - Hands-On Demo

Lecture 12 AWS Lake Formation

Lecture 13 AWS Lake Formation - Hands-On Demo

Lecture 14 Amazon Managed Service for Apache Flink

Lecture 15 Amazon OpenSearch Service

Lecture 16 Amazon OpenSearch Service - Hands-On Demo

Lecture 17 Amazon QuickSight

Lecture 18 Amazon QuickSight - Hands-On Demo

Section 3: Compute

Lecture 19 AWS Batch

Lecture 20 AWS Batch - Hands-On Demo

Lecture 21 Amazon EC2

Lecture 22 Amazon EC2 - Hands-On Demo

Lecture 23 AWS Lambda

Lecture 24 AWS Lambda - Hands-On Demo

Section 4: Containers

Lecture 25 Amazon Elastic Container Registry (Amazon ECR)

Lecture 26 Amazon Elastic Container Registry (Amazon ECR) - Hands-On Demo

Lecture 27 Amazon Elastic Container Service (Amazon ECS)

Lecture 28 Amazon Elastic Container Service (Amazon ECS) - Hands-On Demo

Lecture 29 Amazon Elastic Kubernetes Service (Amazon EKS)

Lecture 30 Amazon Elastic Kubernetes Service (Amazon EKS) - Hands-On Demo

Lecture 31 AWS Fargate

Section 5: Database

Lecture 32 Amazon Redshift

Lecture 33 Amazon Redshift - Hands-On Demo

Section 6: Internet of Things

Lecture 34 AWS IoT Greengrass

Section 7: Machine Learning

Lecture 35 Amazon Bedrock

Lecture 36 Amazon Bedrock - Hands-On Demo

Lecture 37 Amazon Comprehend

Lecture 38 Amazon Comprehend - Hands-On Demo

Lecture 39 Amazon Forecast

Lecture 40 Amazon Fraud Detector

Lecture 41 Amazon Fraud Detector - Hands-On Demo

Lecture 42 Amazon Lex

Lecture 43 Amazon Lex - Hands-On Demo

Lecture 44 Amazon Kendra

Lecture 45 Amazon Kendra - Hands-On Demo

Lecture 46 Amazon Polly

Lecture 47 Amazon Polly - Hands-On Demo

Lecture 48 Amazon Rekognition

Lecture 49 Amazon Rekognition - Hands-On Demo

Lecture 50 Amazon SageMaker

Lecture 51 Amazon SageMaker - Hands-On Demo

Lecture 52 Amazon Textract

Lecture 53 Amazon Textract - Hands-On Demo

Lecture 54 Amazon Transcribe

Lecture 55 Amazon Transcribe - Hands-On Demo

Lecture 56 Amazon Translate

Lecture 57 Amazon Translate - Hands-On Demo

Section 8: Management and Governance

Lecture 58 AWS CloudTrail

Lecture 59 AWS CloudTrail - Hands-On Demo

Lecture 60 Amazon CloudWatch

Lecture 61 Amazon CloudWatch - Hands-On Demo

Section 9: Networking and Content Delivery

Lecture 62 Amazon VPC - Part 1

Lecture 63 Amazon VPC - Part 2

Lecture 64 Amazon VPC - Hands-On Demo

Section 10: Security, Identity, and Compliance

Lecture 65 AWS Identity and Access Management (IAM) - Part 1

Lecture 66 AWS Identity and Access Management (IAM) - Part 2

Lecture 67 AWS Identity and Access Management (IAM) - Hands-On Demo

Section 11: Storage

Lecture 68 Amazon Elastic Block Store (Amazon EBS)

Lecture 69 Amazon Elastic Block Store (Amazon EBS) - Hands-On Demo

Lecture 70 Amazon Elastic File System (Amazon EFS)

Lecture 71 Amazon Elastic File System (Amazon EFS) - Hands-On Demo

Lecture 72 Amazon FSx

Lecture 73 Amazon Simple Storage Service (S3) - Part 1

Lecture 74 Amazon Simple Storage Service (S3) - Part 2

Lecture 75 Amazon Simple Storage Service (S3) - Part 3

Lecture 76 Amazon Simple Storage Service (S3) - Part 4

Lecture 77 Amazon S3 Hands-On Demo

Section 12: Practice Exam

Data scientists, ML engineers, and cloud professionals preparing for the AWS Certified Machine Learning - Specialty (MLS-C01) exam, or anyone with AWS ML Associate certification ready to advance. Also suitable for developers and IT professionals transitioning to MLOps roles who need hands-on experience with AWS's complete ML ecosystem.