Foundations of AI and Machine Learning

This course is part of Microsoft AI & ML Engineering Professional Certificate

Instructor: Microsoft

Skills you'll gain

  •   MLOps (Machine Learning Operations)
  •   PyTorch (Machine Learning Library)
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Data Processing
  •   Application Deployment
  •   Data Security
  •   Data Management
  •   Artificial Intelligence
  •   Tensorflow
  •   Application Frameworks
  •   Scikit Learn (Machine Learning Library)
  •   Data Cleansing
  •   Scalability
  •   Cloud Infrastructure
  •   Machine Learning
  •   Data Pipelines
  •   Infrastructure Architecture
  • There are 5 modules in this course

    By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and their interrelationships. 2. Analyze, describe, and critically discuss efficient data pipelines for AI & ML workflows. 3. Analyze and evaluate model development frameworks for various AI & ML applications. 4. Prepare AI & ML models for deployment in production environments. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.

    Data management in AI/ML

    Considering and selecting model frameworks

    Considerations when deploying platforms

    AI/ML concepts in practice

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