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Master Machine Learning on AWS

Master Machine Learning on AWS


The Machine Learning Pipeline on AWS certification validates the ability to build, train, tune, and deploy machine learning models using AWS services. It's about understanding the mechanisms of data collection, storage, processing, and analysis in order to create insightful predictions. This certification ensures knowledge of AWS machine learning algorithms and their implementation. It's used by businesses across industries to leverage machine learning capabilities to improve efficiency, predictive accuracy, and business outcomes. AWS offers a complete set of tools and services to facilitate this, including data lakes, data warehouses, and business intelligence tools. These skills are essential to stay competitive in the digital economy.

Course Prerequisites

- Basic understanding of Machine Learning
- Knowledge of AWS services and architecture
- Familiarity with programming languages, preferably Python or Java
- Experience with basic SQL and database concepts
- Background in statistics and data analysis
- Practical experience in developing, training, and validating ML models
- AWS Certified Developer or AWS Certified Solutions Architect credential preferred
AWS Machine Learning Pipeline Certification training provides participants a comprehensive understanding of how to build, train, and deploy machine learning models using the AWS Cloud. The coursework covers major topics including data exploration and visualization, cleaning, preprocessing, model validation, and AWS Machine learning services. Participants learn to leverage data using cloud-based, scalable machine learning algorithms and work with real-world, complex datasets for practical, hands-on experience. The course is ideal for developers, data scientists, and IT professionals.


  • 1 Section
  • 8 Lessons
  • 0 Quizzes
  • 32h Duration
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Master Machine Learning on AWS
8 Lessons0 Quizzes
  1. Introduction to Machine Learning and the ML Pipeline
  2. Introduction to Amazon SageMaker
  3. Problem Formulation
  4. Preprocessing
  5. Model Training
  6. Model Evaluation
  7. Feature Engineering and Model Tuning
  8. Deployment
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