What you'll learn:
- Select and justify the appropriate ML approach for a given business problem
- Identify appropriate AWS services to implement ML solutions
- Design and implement scalable, cost-optimized, reliable, and secure ML solutions
- The ability to express the intuition behind basic ML algorithms
- Performing hyperparameter optimisation
- Machine Learning and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment best practices
- The ability to follow operational best practices
Prepare for the AWS Certified Machine Learning – Specialty (MLS-C01) exam in 2024 with our comprehensive and updated course. Dive deep into machine learning concepts and applications on the AWS platform, equipping yourself with the skills needed to excel in real-world scenarios. Master techniques, data preprocessing, and utilize popular AWS services such as Amazon SageMaker, AWS Lambda, AWS Glue, and more.
Our structured learning journey aligns with the exam's domains, ensuring thorough preparation for certification success and practical application of machine learning principles.
Key Skills and Topics Covered:
Choose and justify ML approaches for business problems
Identify and implement AWS services for ML solutions
Design scalable, cost-optimized, reliable, and secure ML solutions
Skillset requirements: ML algorithms intuition, hyperparameter optimization, ML frameworks, model-training, deployment, and operational best practices
Domains and Weightage:
Data Engineering (20%): Create data repositories, implement data ingestion, and transformation solutions using AWS services like Kinesis, EMR, and Glue.
Exploratory Data Analysis (24%): Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.
Modeling (36%): Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.
Machine Learning Implementation and Operations (20%): Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.
Detailed Learning Objectives:
Data Engineering: Create data repositories, implement data ingestion and transformation solutions using AWS services like Kinesis, EMR, and Glue.
Exploratory Data Analysis: Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.
Modeling: Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.
ML Implementation and Operations: Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.
Tools, Technologies, and Concepts Covered:
Ingestion/Collection, Processing/ETL, Data analysis/visualization, Model training, Model deployment/inference, Operational
AWS ML application services, Python language for ML, Notebooks/IDEs
AWS Services Covered:
Analytics: Amazon Athena, Amazon EMR, Amazon QuickSight, etc.
Compute: AWS Batch, Amazon EC2, etc.
Containers: Amazon ECR, Amazon ECS, Amazon EKS, etc.
Database: AWS Glue, Amazon Redshift, etc.
IoT: AWS IoT Greengrass
Machine Learning: Amazon SageMaker, AWS Deep Learning AMIs, Amazon Comprehend, etc.
Management and Governance: AWS CloudTrail, Amazon CloudWatch, etc.
Networking and Content Delivery, Security, Identity, and Compliance: Various AWS services.
Serverless: AWS Fargate, AWS Lambda
Storage: Amazon S3, Amazon EFS, Amazon FSx
For the learners who are new to AWS, we have also added basic tutorials to get it up and running.
Unlock unlimited potential in 2024! Master AI-powered insights on AWS with our Machine Learning Specialty course. Get certified and elevate your career!
