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Exam Readiness: AWS Certified Machine Learning – Specialty (MLS-C01)

This course prepares you to take the AWS Certified Machine Learning – Specialty exam, which validates your ability to design, implement, deploy, and maintain machine learning (ML) solutions.

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This course prepares you to take the AWS Certified Machine Learning – Specialty exam, which validates your ability to design, implement, deploy, and maintain machine learning (ML) solutions.

In this course, you’ll learn about the logistics of the exam and the mechanics of exam questions, and you’ll explore the exam’s technical domains.

You’ll review core AWS services and key concepts for the exam domains:

1) Data Engineering

2)Exploratory Data Analysis

3)Modeling

4) Machine Learning Implementation and Operations

You’ll also learn key test-taking strategies and will put them into action, taking multiple study questions. Once you’ve honed your skills, you’ll have the chance to take a quiz that will help you assess your areas of strength and weakness, so that you’ll know what to emphasize in your pre-exam studies.

Course objectives:

By the end of this course, you will be able to:

Identify your strengths and weaknesses in each exam domain so that you know what to focus on when studying for the exam
Describe the technical topics and concepts that make up each of the exam domains
Summarize the logistics and mechanics of the exam and its questions
Use effective strategies for studying and taking the exam

Intended audience:

This course is intended for:

ML practitioners who have at least one year of practical experience, and who are preparing to take the AWS Certified Machine Learning – Specialty exam

Prerequisites:

We recommend that attendees of this course have:

Proficiency in expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization
Understanding of the ML pipeline and its components
Experience with ML and deep learning frameworks
Understanding of and experience in model training, deployment, and operational best practices [Enroll] (www.aws.training)

Course outline:

Module 0:

Course Introduction:

Module 1: Exam Overview and Test-taking Strategies:

Exam overview, logistics, scoring, and user interface
Question mechanics and design
Test-taking strategies

Module 2:

Domain 1 – Data Engineering:

Domain 1.1: Data Repositories for ML
Domain 1.2: Identify and implement a data-ingestion solution
Domain 1.3: Identify and implement a data-transformation solution
Walkthrough of study questions
Domain 1 quiz

Module 3:

Domain 2 – Exploratory Data Analysis:

Domain 2.1: Sanitize and prepare data for modeling
Domain 2.2: Perform featuring engineering
Domain 2.3: Analyze and visualize data for ML
Walkthrough of study questions
Domain 2 quiz

Module 4:

Domain 3 – Modeling:

Domain 3.1: Frame business problems as ML problems
Domain 3.2: Select the appropriate model(s) for a given ML problem
Domain 3.3: Train ML models
Domain 3.4 Perform hyperparameter optimization
Domain 3.5 Evaluate ML models
Walkthrough of study questions
Domain 3 quiz

Module 5:

Domain 4 – ML Implementation and Operations:

Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem
Domain 4.3: Apply basic AWS security practices to ML solutions
Domain 4.4: Deploy and operationalize ML solutions
Walkthrough of study questions
Domain 4 quiz

Module 6: Additional Study Questions: