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Microsoft

Machine Learning for Beginners - A Curriculum

Microsoft via Independent

Overview

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Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!

Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

Each lesson includes:

  • optional sketchnote
  • optional supplemental video
  • video walkthrough (some lessons only)
  • pre-lecture warmup quiz
  • written lesson
  • for project-based lessons, step-by-step guides on how to build the project
  • knowledge checks
  • a challenge
  • supplemental reading
  • assignment
  • post-lecture quiz

Syllabus

  1. Introduction to machine learning
  2. The History of machine learning
  3. Fairness and machine learning
  4. Techniques for machine learning
  5. Introduction to regression
  6. North American pumpkin prices 🎃
  7. A Web App 🔌
  8. Introduction to classification
  9. Delicious Asian and Indian cuisines 🍜
  10. Introduction to clustering
  11. Exploring Nigerian Musical Tastes 🎧
  12. Introduction to natural language processing ☕️
  13. Common NLP Tasks ☕️
  14. Translation and sentiment analysis ♥️
  15. Romantic hotels of Europe ♥️
  16. Introduction to time series forecasting
  17. ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA
  18. ⚡️ World Power Usage ⚡️ - time series forecasting with SVR
  19. Introduction to reinforcement learning
  20. Help Peter avoid the wolf! 🐺
  21. Real-World ML scenarios and applications
  22. Model Debugging in ML using RAI dashboard

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