MyVid: Full applied AI & Machine Learning tutorials

If you would like to understand more about what is Artificial Intelligence, Machine Learning, and how to apply this in your code. I suggest you to follow this video series from Cassie Kozyrkov, a very well known Data Decision Intelligence expert in Google.

1st Video – Introduction to Machine Learning & Artificial Intelligence :

2nd Video – Life of an Artificial Intelligence Project :

3rd Video – Taking your AI project from Prototype to Production :

4th Video – Guide to Artificial Intelligence Algorithms :

I personally really learnt a lot from her videos, especially on how to explain ML to public.
To watch the videos in byte size, based on the topic that you preferred. Here are the list:

Basic Concepts

  1. Basics: MFML 000 – Welcome
  2. Basics: MFML 001 – What is machine learning?
  3. Basics: MFML 002 – Why use machine learning?
  4. Basics: MFML 003 – How does machine learning work?
  5. Basics: MFML 004 – How to test ML
  6. Basics: MFML 005 – What’s inside the black box?
  7. Basics: MFML 006 – Simple linear regression
  8. Basics: MFML 007 – Multiple linear regression
  9. Basics: MFML 008 – Feature engineering
  10. Basics: MFML 009 – What is AI?
  11. Basics: MFML 010 – Why did we wait so long for AI?
  12. Basics: MFML 011 – Geoff, Fei-Fei, and Jeff
  13. Basics: MFML 012 – Real applications
  14. Basics: MFML 013 – How to find good AI use cases
  15. Basics: MFML 014 – Human creativity in AI
  16. Basics: MFML 015 – How Do GANs work?
  17. Basics: MFML 016 – Why trust AI?
  18. Basics: MFML 017 – Explainability and AI
  19. Basics: MFML 018 – Intro to training, validation, and testing
  20. Basics: MFML 019 – How to avoid machine learning pitfalls
  21. Basics: MFML 020 – Decision Intelligence
  22. Basics: MFML 021 – Why do businesses fail at machine learning?
  23. Basics: MFML 022 – Skilled decision-makers
  24. Basics: MFML 023 – Reliable or unreliable?
  25. Basics: MFML 024 – Preventable disasters
  26. Basics: MFML 025 – Wish responsibly
  27. Basics: MFML 026 – AI is a team sport!
  28. Basics: MFML 027 – Our AI future

The 12 Steps of AI Step 0: Reality check

  1. Step 0: MFML 028 – The 12 steps of AI
  2. Step 0: MFML 029 – Where to start with applied AI? 
  3. Step 0: MFML 030 – Classification vs regression
  4. Step 0: MFML 031 – Instances, features, and targets
  5. Step 0: MFML 032 – Supervised learning
  6. Step 0: MFML 033 – Unsupervised learning
  7. Step 0: MFML 034 – Semi-supervised learning
  8. Step 0: MFML 035 – Reinforcement learning
  9. Step 0: MFML 036 – What on earth is data science?
  10. Step 0: MFML 037 – Data science flowchart
  11. Step 0: MFML 038 – Don’t forget data!

Step 1: Define your objectives

  1. Step 1: MFML 039 – What is “good behavior” for AI?
  2. Step 1: MFML 040 – False positives and true negatives
  3. Step 1: MFML 041 – Confusion matrix
    Step 1: MFML 042 – Performance metrics
  4. Step 1: MFML 043 – Ground truth
  5. Step 1: MFML 044 – Precision vs recall
  6. Step 1: MFML 045 – What is optimization?
  7. Step 1: MFML 046 – Loss functions
  8. Step 1: MFML 047 – Setting launch criteria

Step 2: Get access to data

  1. Step 2: MFML 048 – Data engineering

Step 3: Split your data

Step 4: Explore your data

Step 5: Prepare your tools

Step 6: Use your tools to train some models

Step 7: Debug, analyze, and tune

Step 8: Validate your models

Step 9: Test your model

Step 10: Productionize your system

Step 11: Run live experiments to launch safely

Step 12: Monitor and maintain… forever

Enjoy!

Kind Regards,
Doddi Priyambodo

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