AI and Machine Learning Algorithms and Techniques

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

Instructor: Microsoft

Skills you'll gain

  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Business Logic
  •   Predictive Modeling
  •   Feature Engineering
  •   Machine Learning Algorithms
  •   Large Language Modeling
  •   Artificial Neural Networks
  •   Statistical Machine Learning
  •   Deep Learning
  •   Reinforcement Learning
  •   Decision Tree Learning
  •   Data Modeling
  •   Generative AI
  •   Unsupervised Learning
  •   Performance Metric
  •   Applied Machine Learning
  •   Dimensionality Reduction
  •   Supervised Learning
  • There are 5 modules in this course

    By the end of this course, you will be able to: 1. Implement, evaluate, and explain supervised, unsupervised, and reinforcement learning algorithms. 2. Apply feature selection and engineering techniques to improve model performance. 3. Describe deep learning models for complex AI tasks. 4. Assess the suitability of various AI & ML techniques for specific business problems. 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.

    Unsupervised learning

    Reinforcement learning and other approaches

    Deep learning and neural networks

    The concepts in practice

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