Who else is thinking about self-learning machine learning? If so, for what purpose? What made you decide to undertake this project? Here are my two main reasons: 1) I need to find a way to connect my nanomagnetism research and ML. 2) To make my resume stand out, once I’m ready to look for a job in the industry after I’m done with my MSc (and perhaps followed by a PhD).
@@vaibhavsatish413 Great! How is it going so far? Challenging or ok? People might be interested to know different perspectives, depending on the previous math/coding background of the person trying to self-learn ML. Thank you for your comment, btw! :)
@@juliaifrank Well, for me personally it is just challenging enough so far. Which is like the perfect amount of difficulty. It does require decent familiarity with python(list comprehension and class inheritance**), linear algebra and maths behind ML algorithms. I am a Data Science and Statistics undergrad, so I have done these through academic courses and youtube videos. But even if you have done these courses in the past but are unsure if your knowledge is still intact, then,I think you can still start with this book. You’ll be able to pick things up as you code along consistently using this book. Also, while following along with the coding portions, I would suggest to keep documentation of libraries like numpy, pandas, matplotlib and sklearn handy with you (especially sklearn). This is to try and experiment with code yourself and to learn to read documentations. In case, you are unable to figure out what certain code is doing then make chatgpt explain it to you. That will promote speed learning. PS. Don’t forget to journal your learnings :) PPS. Excuse my lengthy reply.
@@vaibhavsatish413 thank you very much for this lengthy reply!! That’s very helpful to me and to whoever is interested in following along. It’s always great to know someone who is a few steps ahead:)
I had read Geron's book but then realized that book was very intense for me because I did not know Python and maths such as Linear Algebra, Calculus, and Probability. So, I stopped reading the book and started to learn Python from the scratch (I am learning functions). After that, I will jump on some maths. Next, Python's libraries such as Numpy, Pandas, and Matplotlib. This might be a long journey, but I want to start strong. My purpose is either work in an enterprise or in academia.
Greetings! Interestingly, I bought almost same books before watching your video! I got myself a copy of "Hands-On Machine Learning" and a book about SQL. By the way, "Simple SQL" also has an accompanying volume "Mastering SQL Joins". Sebastian Raschka has a new book: "Machine Learning Q & A". As far as I understand, SQL would provide tools for data extraction from the database. Then these data can be used for ML.
Who else is thinking about self-learning machine learning? If so, for what purpose? What made you decide to undertake this project?
Here are my two main reasons:
1) I need to find a way to connect my nanomagnetism research and ML.
2) To make my resume stand out, once I’m ready to look for a job in the industry after I’m done with my MSc (and perhaps followed by a PhD).
I am 138 pages in in O'Reilly's ML book. ✋
Purpose: To be able to build my own ML systems
@@vaibhavsatish413 Great! How is it going so far? Challenging or ok?
People might be interested to know different perspectives, depending on the previous math/coding background of the person trying to self-learn ML.
Thank you for your comment, btw! :)
@@juliaifrank Well, for me personally it is just challenging enough so far. Which is like the perfect amount of difficulty. It does require decent familiarity with python(list comprehension and class inheritance**), linear algebra and maths behind ML algorithms. I am a Data Science and Statistics undergrad, so I have done these through academic courses and youtube videos.
But even if you have done these courses in the past but are unsure if your knowledge is still intact, then,I think you can still start with this book. You’ll be able to pick things up as you code along consistently using this book.
Also, while following along with the coding portions, I would suggest to keep documentation of libraries like numpy, pandas, matplotlib and sklearn handy with you (especially sklearn). This is to try and experiment with code yourself and to learn to read documentations. In case, you are unable to figure out what certain code is doing then make chatgpt explain it to you. That will promote speed learning.
PS. Don’t forget to journal your learnings :)
PPS. Excuse my lengthy reply.
@@vaibhavsatish413 thank you very much for this lengthy reply!! That’s very helpful to me and to whoever is interested in following along.
It’s always great to know someone who is a few steps ahead:)
I had read Geron's book but then realized that book was very intense for me because I did not know Python and maths such as Linear Algebra, Calculus, and Probability. So, I stopped reading the book and started to learn Python from the scratch (I am learning functions). After that, I will jump on some maths. Next, Python's libraries such as Numpy, Pandas, and Matplotlib. This might be a long journey, but I want to start strong. My purpose is either work in an enterprise or in academia.
Greetings!
Interestingly, I bought almost same books before watching your video! I got myself a copy of "Hands-On Machine Learning" and a book about SQL. By the way, "Simple SQL" also has an accompanying volume "Mastering SQL Joins". Sebastian Raschka has a new book: "Machine Learning Q & A". As far as I understand, SQL would provide tools for data extraction from the database. Then these data can be used for ML.
Hi! Thanks for leaving a comment.
So you are also on a machine learning journey? ;) How is it going so far?