Description
AWS Certified Machine Learning Specialty Course 2024 – Hands On (V2). Prepare for the AWS Certified Machine Learning – Specialty exam 2024 (MLS-C01) with our comprehensive and up-to-date course. Dive deep into the concepts and applications of machine learning on the AWS platform and equip yourself with the skills you need to excel in real-world scenarios. Master the techniques, data preprocessing, and use of popular AWS services like Amazon SageMaker, AWS Lambda, AWS Glue, etc. Our structured learning journey is aligned with the exam domains and ensures thorough preparation for certification success and practical application of machine learning principles.
Key qualifications and topics covered:
- Select and justify ML approaches to business problems
- Identify and implement AWS services for ML solutions
- Design scalable, optimized, reliable and secure ML solutions
- Skill requirements: Intuition for ML algorithms, hyperparameter tuning, ML frameworks, model training, deployment, and operational best practices
Range and weight:
- Data Engineering (20%): Create data repositories, implement data migration and data transformation solutions using AWS services such as Kinesis, EMR and Glue.
- Exploratory data analysis (24%): Cleaning and preparing data, performing feature engineering, and analyzing/visualizing data for ML using techniques such as clustering and descriptive statistics.
- Modeling (36%): Formulating business problems, selecting appropriate models, training models, performing hyperparameter optimization, and evaluating ML models using various metrics.
- Machine Learning Implementation and Operations (20%): Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services such as CloudWatch, SageMaker, and security best practices.
Detailed educational objectives:
- Data Engineering: Create data repositories, implement data collection and transformation solutions using AWS services such as Kinesis, EMR, and Glue.
- Exploratory data analysis: Clean and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.
- Modeling: Formulate business problems, select appropriate models, train models, optimize metaparameters, and evaluate ML models using various metrics.
- ML Implementation and Operations: Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services such as CloudWatch, SageMaker, and security best practices.
Tools, technologies, and concepts covered: Ingestion/capture, processing/ETL, data analysis/visualization, model training, model deployment/inference, operations. AWS ML Application Services, Python Language for ML, Notebook/IDE
AWS services covered:
- Analytics: Amazon Athena, Amazon EMR, Amazon QuickSight, etc.
- Computing: AWS Batch, Amazon EC2, etc.
- Containers: Amazon ECR, Amazon ECS, Amazon EKS, etc.
- Database: AWS Glue, Amazon Redshift, etc.
- Internet of Things: AWS IoT Greengrass
- Machine Learning: Amazon SageMaker, AWS Deep Learning AMIs, Amazon Understand, 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
What you will learn in the AWS Certified Machine Learning Specialty 2024 – Hands On(V2) course
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Select and justify the appropriate ML approach for a specific business problem
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Identify the right AWS services to implement ML solutions
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Design and implement scalable, optimized, reliable and secure ML solutions
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Ability to express the intuition behind basic ML algorithms
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Perform hyperparameter optimization
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Frameworks for machine learning and deep learning
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Ability to follow best practices for model training
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Ability to follow deployment best practices
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Ability to follow best operating practices
This course is suitable for people who
- Anyone interested in cloud-based machine learning and data science on AWS
- Anyone preparing for the AWS Certified Machine Learning – Expert exam
- Anyone who wants to learn best practices for deploying machine learning models in the cloud
AWS Certified Machine Learning Specialty 2024 Course Specifications – Hands On (V2)
- Editor: Udemy
- Teacher: Diverse AI learning
- Training level: beginner to advanced
- Training duration: 34 hours and 0 minutes
- Number of courses: 172
AWS Certified Machine Learning Specialty 2024 – Hands On(V2) course topics on 3/2024
AWS Certified Machine Learning Specialty 2024 Course Prerequisites – Hands On (V2)
- Basic knowledge of AWS
- Basic knowledge of Python programming
- Basic understanding of data science
- Basic knowledge of machine learning
Course pictures
Sample video of the course
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