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My journey into learning machine and deep learning

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By Watsh Rajneesh 3 min read

I read the book by Radek Osmulski called Meta Learning in which he describes how he planned to learn programming and then machine learning and with his hard work and dedication he has been able to succeed in becoming an ML Engineer at Nvidia. I learned about this book from fast.ai site and Radek was one of the students of that course. In the book he mentions of learning publicly and by which he means to be able to record what one is learning on some blog or other social media. As a result of doing that, one becomes more accountable for their commitments to learning (or whatever they plan to achieve). It does not matter who ends up reading that blog or tweet but the very fact that one is writing it down as a public record makes one think about the tasks put out their as something they need to give their best to complete. I also read another book Can’t Hurt Me where the author David Goggins talks about his accountability mirror. There was a patten of doing some hard thing like learning some hard topic or completing seal training publicly and as a result be more accountable for doing one’s part to completing those tasks.

That said, i started with looking for a good theme to build my site. I have a blog site which i have not been regular to for last couple years but that has limitations in terms of putting code or even images. So i wanted to use a markdown based site hosted on github pages. I found Chirpy theme pretty feature rich for a free theme and used it to build this site. On this site i will be putting up my plans, learnings and any other experiences which i may want to reflect upon in future.

Coming up with a list of all books and courses and hands-on practices needed to master the skills of machine learning and deep learning. I will keep this list updated as i make progress with reading the books or doing the courses.

I am arranging the books, courses and hands-on practices in sections and keeping them in the order in which to cover them. Will keep checking them off as i complete them.

Goal is to be able to complete all of the below listed books, courses and Hands-on practices by 2024 year end.

Books

  • Machine Learning with PyTorch & Scikit-Learn by Sebastian Raschka (ETA 5/30) <–
  • Deep Learning for Coders with fastai and PyTorch by Jeremy Howard (ETA 6/30)
  • Learning Deep Learning by Ekman (ETA 7/30)
  • Data Science from Scratch by Joel Grus (ETA 8/30)
  • Deep Learning from Scratch by Weidman (ETA 9/30)
  • Python for Data Analysis 3E by Wes Mckinney (ETA 10/30)
  • Learning from Data - A Short Course by Yaser Abu-Mostafa (ETA 11/30)
  • Building Machine Learning Powered Applications by Ameisen (ETA 12/30)

Courses

Practice

  • Kaggle (Start by 7/01 after completing fastai course work)

Updated Plan (4/27/24)

  1. Applied Data Science with Python course + Python for Data Analysis + Hands-on Machine Learning - 5/30
  2. ML specialization - will have certificate to show here - 5/30
  3. Math for Machine learning course + Deep learning with Python - 7/30
  4. DL specialization - will have certificate to show here. 7/30
  5. Gen AI with LLM course - 7/30
  6. Fast.ai course + Deep Learning for coders book 8/30
ML, Journey
plan ml
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