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4+ Hours of Video Instruction
Learn the techniques and approaches to successfully pass the AWS Certified Machine Learning - Specialty Exam with hands-on exercises.
Getting the AWS Certified Machine Learning - certification highlights your versatility as an ML engineer. Usually, ML engineers focus on handling data and building models, so if you know can use cloud tools, it makes you an even more valuable as an MLOps engineer. Youll be able to ingest your own data, get through the feature engineering process, train and evaluate models, and deploy them to where they will be consumed. This certification shows that you know how to do full-stack ML development.
In this series of videos, author Milecia McGregor shares a mix of slides and demonstrations in AWS, along with some examples in Visual Studio with Python. Its just what you need to learn to pass the exam. It includes an overview of concepts with hands-on work using AWS tools like Kinesis and EMR.
Skill Level:
Learn How To:
Course requirement:
Who Should Take This Course:
Job titles: ML engineer, DevOps engineer, Aspiring ML engineer
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Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.
Introduction
Lesson 1: Data Engineering
1.1 Create data repositories for machine learning
1.2 Identify and implement a data ingestion solution
1.3 Decide between ingestion tools
1.4 Identify and implement a data transformation solution
1.5 Get some practice: questions and exercises
Lesson 2: Exploratory Data Analysis
2.1 Sanitize and prepare data for modeling
2.2 Perform feature engineering
2.3 Analyze data for machine learning
2.4 Visualize data for machine learning
2.5 Get some practice: questions and exercises
Lesson 3: Training Models
3.1 Frame business problems as machine learning problems
3.2 Select the appropriate model for a machine learning problem
3.3 Understand the intuition behind the model
3.4 Train machine learning models
3.5 Choose compute option
3.6 Get some practice: questions and exercises
Lesson 4: Evaluating Models
4.1 Perform hyperparameter optimization
4.2 Use other methods for hyperparameter optimization
4.3 Evaluate machine learning models
4.4 Compare models with different metrics
4.5 Implement machine learning best practices
4.6 Get some practice: questions and exercises
Lesson 5: Machine Learning Implementation and Operations
5.1 Build machine learning solutions for production
5.2 Address scaling concerns
5.3 Recommend and implement the appropriate machine learning services
5.4 Apply basic AWS security practices to machine learning solutions
5.5 Deploy and operationalize machine learning solutions
5.6 Get some practice: questions and exercises
Summary