AWS Certified Machine Learning: Specialty (MLS-C01)

On this accelerated AWS Certified Machine Learning - Specialty course, you’ll learn how to design, implement, deploy and maintain machine learning (ML) models and solutions using the AWS Cloud.

In just 3 days, you'll learn how to support your business' growth and deter from external threats by choosing appropriate ML approaches and AWS services to implement ML solutions. You'll also build knowledge on:

• Feature engineering
• Analysing and visualising data for machine learning
• Hyperparameter optimisation

At the end of the course, you'll sit the AWS Certified Machine Learning - Specialty (MLS-C01) exam. You'll get access to official courseware and learn from certified AWS instructors as Firebrand is an official training provider.

Through Firebrand's Lecture | Lab | Review methodology, you'll learn at twice the speed in a controlled and focused classroom environment.

Audience

If you're in a development or data science role and have experience in machine learning, this course is for you.

• Module 1: Data Engineering
• Module 2: Exploratory Data Analysis
• Module 3: Modelling
• Module 4: Machine Learning Implementation and Operations

Before attending this accelerated course, it's recommended you possess that following:

• One to two years' experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud
• The ability to express the intuition behind basic ML algorithms
• Experience performing basic hyperparameter optimization
• Experience with ML and deep learning frameworks
• The ability to follow model-training best practices
• The ability to follow deployment and operational best practices

As part of your accelerated course, you'll sit the following exam at the Firebrand Training centre, covered by your Certification Guarantee:

AWS Certified Machine Learning - Specialty (MLS-C01) exam

• Exam code: MLS-C01
• Format: Multiple-choice and multiple-answer
• Passing score: 750 (scale of 100-1000)
• Duration: 170 minutes
• Languages: English, Japanese, Korean and simplified Chinese
• Domains:
1. Data Engineer (20%)
2. Exploratory Data Analysis (24%)
3. Modelling (36%)
4. Machine Learning Implementation and Operations (20%)