• views: 121
• date: 2 January 2025
• posted by: voska89

What you'll learn
Prepare for the AWS Certified Machine Learning Engineer Associate exam.
Data Engineering
Perform data preparation, feature engineering for ML models.
Exploratory Data Analysis
Requirements
It is recommended to have at least 1 year of experience in AWS
Description
The AWS Certified Machine Learning Engineer Associate certification is designed for professionals seeking to validate their expertise in building, training, and deploying machine learning (ML) models on the AWS platform. This certification caters to individuals with experience in data science, machine learning engineering, or related fields, demonstrating proficiency in both ML principles and AWS services tailored for ML solutions. It underscores skills critical to developing scalable, efficient, and cost-effective ML applications.The certification covers four main domains: Data Engineering, Exploratory Data Analysis (EDA), Modeling, and Machine Learning Implementation and Operations. In the Data Engineering domain, candidates are tested on their ability to design scalable data pipelines, process large datasets, and use AWS services like Amazon S3, Glue, and Redshift to prepare data for analysis. Mastery of data transformation and storage techniques ensures efficient ML workflows. Exploratory Data Analysis focuses on techniques to analyze and visualize data to extract insights. Professionals must demonstrate an understanding of feature engineering, data cleaning, and handling imbalanced datasets.Earning the AWS Certified Machine Learning Engineer Associate certification credential not only validates technical expertise but also positions professionals as leaders in a rapidly growing field. It enables them to contribute effectively to ML projects, helping organizations innovate and achieve measurable outcomes.
Who this course is for
Data engineers, data scientists, DevOps professionals
Homepage

https://www.udemy.com/course/aws-certified-machine-learning-engineer-associate-exam-prep/

   
   
   

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