Udemy – AWS Certified Machine Learning Specialty 2024 – Hands On(V2) 2023-5

Udemy – AWS Certified Machine Learning Specialty 2024 – Hands On(V2) 2023-5 Downloadly IRSpace

Apr 17, 2025 - 19:13 Updated: Apr 21, 2025 - 09:15

0 0

Udemy – AWS Certified Machine Learning Specialty 2024 – Hands On(V2) 2023-5

AWS Certified Machine Learning Specialty Course 2024 – Hands On(V2). Prepare for the 2024 AWS Certified Machine Learning – Specialty exam (MLS-C01) with our comprehensive and up-to-date course. Dive deep into machine learning concepts and applications 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 such as Amazon SageMaker, AWS Lambda, AWS Glue, etc. Our structured learning journey aligns with exam domains, ensuring thorough preparation for certification success and practical application of machine learning principles.

Key skills 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 set requirements: ML algorithm intuition, hyperparameter optimization, ML frameworks, model training, deployment, and operational best practices

Ranges 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%): clean 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 such as CloudWatch, SageMaker, and security best practices.

Detailed educational objectives:

Data Engineering: Create data repositories, implement data ingestion 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: Framing business problems, selecting appropriate models, training models, meta-parameter optimization, and evaluating 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/Collection, Processing/ETL, Data Analysis/Visualization, Model Training, Model Deployment/Inference, Operational. AWS ML Application Services, Python Language for ML, Notebook/IDE

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
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: different 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

Choose and justify the appropriate ML approach for a given business problem
Identify the right AWS services to implement ML solutions
Design and implement scalable, optimized, reliable and secure ML solutions
Ability to express the intuition behind basic ML algorithms
Perform hyperparameter optimization
Machine learning and deep learning frameworks
Ability to follow model training best practices
Ability to follow deployment best practices
Ability to follow best operating practices

This course is suitable for people who

Anyone interested in AWS cloud-based machine learning and data science
Anyone preparing for AWS Certified Machine Learning – Expert exam
Anyone looking to learn best practices for deploying machine learning models in the Cloud