AWS Machine Learning Certification

AWS Machine Learning Certification

AWS Machine Learning Certification

The AWS Machine learning Certificate is particularly designed for the data scientists and developers who want to actively demonstrate and validate their skills of Machine learning on the AWS Platform. In the exam, the candidate is tested for his ability to design, build, deploy, and maintain machine learning solutions. In this, the 4 wide domains are covered that include Data Engineering, Exploratory Data analysis, modelling, and machine learning implementation and operations This path’s information and resources will be critical in preparing for the exam of AWS Certified Machine Learning Specialty. In this course, the candidates will also learn to apply Artificial intelligence, machine learning, and deep learning that will unlock their business insights.

The new AWS Machine Learning Certification exam code is MLS-C01.


  • The candidate must have one to two years of hands-on experience architecting, developing, or running machine learning workloads in AWS
  • understanding at associate level of AWS services such as EC2.
  • Some prior experience with machine learning
  • To complete the hands-on lab exercise, you must have an AWS account.
  • What skills are validated by this course?
  • Choose and justify the best ML approach for a given business issue.
  • Determine the best AWS services for implementing ML solutions.
  • Create and implement scalable, cost-effective, dependable, and secure machine learning solutions.

What will you learn in this course?

  • What can you expect from the AWS Certified Machine Learning Specialty exam?
  • The machine learning algorithms built into Amazon SageMaker (XGBoost, BlazingText, Object Detection, etc.)
  • Techniques for feature engineering such as imputation, outliers, binning, and normalisation
  • High-level machine learning services: Understand, Translate, Polly, Transcribe, Lex, Rekognition, and other terms
  • S3, Glue, Kinesis, and DynamoDB Data Engineering
  • Data exploration using scikit learn, Athena, Apache Spark, and EMR
  • Deep learning and deep neural network hyperparameter tuning
  • Automatic model tuning and SageMaker L1 and L2 regularisation operations
  • Implementing best practises in security to machine learning pipelines

What are Requirements for this course:


  • The learner has 2 at least one or maximum 2 years of experience developing, architecting, or running machine learning/deep learning workloads on the AWS Cloud is required.
  • The ability to demonstrate the intuition underlying basic machine learning algorithms.
  • Extensive experience with basic hyperparameter optimization
  • Experience with machine learning and deep learning frameworks
  • The ability to adhere to best practises in model-training
  • The skill to adhere to best deployment and operational practises

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