This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. The course offers the following tools to help users pass the exam:

• Quizzes: Each lesson module includes a five-question multiple-choice quiz in the style of the AWS ML exam. There are 39 quizzes, with a total of 195 questions.
• Practice Exam: A multiple-choice practice exam is included in the style of the AWS ML exam. This exam totals 50 questions and takes one hour to complete.

Description

This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment.

• Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.* Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.
• Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.
• Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.

Each module offers module quizzes and a practice exam in multiple-choice format.


Skill Level

Intermediate


What You Will Learn

• How to perform data engineering tasks on AWS
• How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS
• How to perform machine learning modeling tasks on the AWS platform
• How to operationalize machine learning models and deploy them to production on the AWS platform
• How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome

Who Should Take This Course

• DevOps engineers who want to understand how to operationalize ML workloads
• Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS
• Machine learning engineers who want to solidify their knowledge about AWS machine learning practices
• Product managers who need to understand the AWS machine learning lifecycle
• Data scientists who run machine learning workloads on AWS

Course Requirements

One to two years of experience with AWS and six months using ML tools. Ideally, candidates will have already passed the AWS Cloud Practitioner certification.