Why this course?

Description

This comprehensive course, "Machine Learning Operations with AWS," offers a deep dive into the essential concepts of AWS Cloud and AWS SageMaker for machine learning operations. Beginning with AWS Cloud essentials, you'll learn the foundations of cloud computing and how to leverage AWS services for ML. The course covers machine learning basics alongside practical implementation using AWS tools and services. You'll also gain valuable insights into version control with Git and automation of deployments using GitHub Actions. Building on this knowledge, you'll explore pipeline creation and MLOps CI/CD deployment using AWS CodePipeline and CodeDeploy. Lastly, the course provides an overview of AWS AI services like Rekognition and Polly, enabling you to harness advanced AI capabilities for your projects.

Part - 1: Introduction to Amazon web services
Day 1 - aws Introduction, account creation
Day 2 - aws cloud essentials 1 - Virtual machines (EC2), ELB, EBS
Day 3 - aws cloud essentials 2 - AWS Lambda, docker container, ECS, EKS
Day 4 - aws cloud Essentials 3 - RDS, DynamoDB, S3

Part - 2: Machine learning
Day 5 - Introduction to machine learning, ML with AWS Sagemaker
Day 6 - Data collection, Pandas for machine learning
Day 7 - Data Engineering with (aws glue, glue ETL, Athena)
Day 8 - Numpy for machine learning
Day 9 - Matplotlib, Seaborn for machine learning
Dat 10 - aws data wrangler
Day 11 - Feature Scaling, encoding, handling null values
Day 12 - Outlier detection & handling, feature selection, train test split
Day 13 - Data preprocessing with SageMaker’s Scikit-Learn Container
Day 14 - Machine learning Modeling, model evaluation
Day 15 - aws Sagemaker built-in Algorithms for ML
Day 16 - Machine learning project - classification - Vehicle insurance claim fraud detection
Day 17 - Machine learning project - Regression - Abalone age prediction
Day 18 - AWS Sagemaker canvas, autopilot
Day 19 - Deep learning introduction
Day 20 - CNN project - Dog / Cat identification project
Day 21 - RNN, NLP basics
Day 22 - NLP project - Clothing - e-commerce review project

Part - 3: Deployment
Day 23 - Version control with Git & Github
Day 24 - CI/CD with Github
Day 25 - Deploying model with aws sagemaker
Day 26 - CI/CD with sagemaker pipeline
Day 27 - Census income prediction project with deployment
Day 28 - Music recommender system project with deployment
Day 29 - AWS AI services - Amazon Rekognition, Comprehend, Polly
Day 30 - AWS AI services - Lex, Transcribe, Textract