An opinionated guide to learning machine learning by Aniedi Udo-Obong
- Subscribe to the Learning Machine Learning newsletter to receive regular updates - https://bit.ly/lml-sgn01
- Intro to ML: ML Zero to Hero (Part 1) - https://bit.ly/lml-001
- Basic Computer Vision: ML Zero to Hero (Part 2) - https://bit.ly/lml-002 (If you watched Part 1 & didn't go on to watch Part 2, this learning machine learning thing may be a bit hectic for you. For every resource I link to, please follow that resource down the rabbit hole i.e. links to related articles, playlists that a video belongs to, suggestions & recommendations etc.)
- Search on Google (or your preferred search engine) for "machine learning" - https://www.google.com/search?q=machine+learning&oq=machine+learning (I always do this when learning something knew or whenever I run in to a bug, error message etc. Read at least the top non-ad post.)
- Read (or study) the Wikipedia entry for machine learning - https://en.wikipedia.org/wiki/Machine_learning (Don't forget to go down the rabbit hole as much as you can).
- Introducing convolutional neural networks: ML Zero to Hero (Part 3) - https://bit.ly/lml-003 (Down the rabbit hole, at least watch Part 4 - https://bit.ly/lml-004x - of this series)
- Learn Python, the most important language for data science & machine learning - https://bit.ly/lml-005 (I prefer Kaggle's introductory courses but the official Python Tutorial, Datacamp, Pluralsight, Udacity, Udemy and a host of others are all excellent resources). So "Why Kaggle? And what is Kaggle?" Your preferred search engine and Wikipedia to the rescue!
- TF 2.x on Kaggle (TF Dev Summit '20) - https://bit.ly/lml-006. For some inspiration (& perspiration) from TF Dev Summit 2020, go further down the rabbit hole and at least watch the keynote on this playlist - https://bit.ly/lml-007
- Watch https://bit.ly/lml-008 or https://bit.ly/lml-009 - if you haven't listened to Jeff Dean ('Yes, "The Jeff Dean"') speak about The Grand Challenges - https://bit.ly/grn-chl. You can also read this article - https://bit.ly/jd-grn-chl-dn - where he shares his thoughts on "How AI Can Solve The Grand Challenges".
- My next best "What is Machine Learning" video would be from Yufeng Guo - https://bit.ly/lml-010. You can watch the entire "AI Adventures" series via - https://bit.ly/lml-010p
- At this point you should have a firm grasp of "Machine Learning 101". I strongly suggest you bookmark/save/download/print these articles for past, present & future reference - https://bit.ly/lml-011a or https://bit.ly/lml-011b
- Robert John's "Bite-Sized Machine Learning Series" - Part 1 (https://bit.ly/lml-rbj01), Part 2 (https://bit.ly/lml-rbj02), Part 3 (https://bit.ly/lml-rbj03) are definitely good reads.
- In some of the previous resources shared, you may have come across or interacted with Colab. "What is Colab?" This playlist - https://bit.ly/lml-012p - should get you started with understanding and using Colab.
If you haven't done so, subscribe to the Learning Machine Learning newsletter to receive regular updates - https://bit.ly/lml-sgn01
--
0. URL to resource: [brief description of content]
-- add your contributions below this line --
0. URL to resource: [brief description of content]
-- add your contributions below this line --
0. URL to resource: [brief description of content]
-- add your contributions below this line --
0. URL to resource: [brief description of content]
-- add your contributions below this line --