My Resources: Math Review For ML: th-cam.com/video/OYJhBjnLp0I/w-d-xo.html How ML Models Learn: th-cam.com/video/bbYdqd6wemI/w-d-xo.html Linear Regression Explained: th-cam.com/video/2vE3DqWdEXo/w-d-xo.html Neural Networks Explained: th-cam.com/video/xZcOTAJ-h6w/w-d-xo.html First-Principles Framework (Learn Fundamentals): www.gptlearninghub.ai/first-principles-framework Beginner's Blueprint (Build Projects): www.gptlearninghub.ai/beginner-blueprint
I personally found buying a 20$ Claude pro subscription and than asking it for a detailed roadmap for whatever I want to learn and than generate detailed notes for each of the module with code is the best strategy to learn today. Trust me no course can give as detailed notes and code as these llm models ( I particularly found Claude to be the best recently) , best part is you can prompt it to simply concepts as much as you want.
Not to mention that they can also answer any question that you want to ask regarding the topic and they can explain it with an analogy, plus they wont judge you 😂
I like learning through books, and I found some great resources ( Python For Data Analysis by Wes McKinsey, Math For ML by Deisenroth; I'm thinking of pairing Andrew Ng's specialisation with Hands On Machine Learning by Aurélein Géron). But I also have the same problem, it takes me more time than what I think I should be investing, like around 1-3 chapters in 2-3 hours based on the length and complexity of the material. It might not seem like much, but I'm studying and preparing for another 4-5 topics rn, so I have little left for it, and I have to practice those concepts by applying them too. But I guess nothing will come out of worrying about it, I'll just have to build my speed over time.
Hi. I wanna learn basics about NLP between Neural Networks and Transformers architecture, for example tokenization, embeddings, RNN's etc. What do you recommend? Book, course (Coursera NLP spec. etc) or something else? Thanks
Karpathy's channel is great for this! I've also created a bunch of free TH-cam videos on NLP concepts like tokenization, embeddings, RNNs, Transformers and more. Check out this playlist: th-cam.com/play/PLf2BgkdQjMYvjsg0DMlMPspibTX6errDc.html&si=kMw4jZ8B9nG1qKG2
I am in 7th grade and I know the basics of Python and I have built some projects now I am switching to java and then c++ but how am I supposed to understand college level math and how can I still learn without needing extremely high level math till I catch up
Wow its great you are so young. The thing is ML is built on mathematics. Its all mathematics in the end. I tell you one thing, my friend is doing masters from top CS schools in my country, and they have rule that you can't take any ML course even if you are CS/AI graduate unless you are done with Linear Algebra, Vector Calculus and Random Process math courses. So, highly suggest you to make Math(language of the universe) as you first love and take part in IMO and IOI selection process in your country and follow your ML passion side by side bit by bit.
Congratulations on getting such a head start! No worries if you're not at college level math yet, just take your time and trust the process, getting 1% better every day.
My Resources:
Math Review For ML: th-cam.com/video/OYJhBjnLp0I/w-d-xo.html
How ML Models Learn: th-cam.com/video/bbYdqd6wemI/w-d-xo.html
Linear Regression Explained: th-cam.com/video/2vE3DqWdEXo/w-d-xo.html
Neural Networks Explained: th-cam.com/video/xZcOTAJ-h6w/w-d-xo.html
First-Principles Framework (Learn Fundamentals): www.gptlearninghub.ai/first-principles-framework
Beginner's Blueprint (Build Projects): www.gptlearninghub.ai/beginner-blueprint
I personally found buying a 20$ Claude pro subscription and than asking it for a detailed roadmap for whatever I want to learn and than generate detailed notes for each of the module with code is the best strategy to learn today. Trust me no course can give as detailed notes and code as these llm models ( I particularly found Claude to be the best recently) , best part is you can prompt it to simply concepts as much as you want.
Not to mention that they can also answer any question that you want to ask regarding the topic and they can explain it with an analogy, plus they wont judge you 😂
Do you need the $20 subscription? Can you not do this with the free version?
LLMs are definitely great study-buddies. Thanks for sharing!
The same way I followed up and asked gpt 4.0 to create a roadmap for me, on the way now 🔥
I like learning through books, and I found some great resources ( Python For Data Analysis by Wes McKinsey, Math For ML by Deisenroth; I'm thinking of pairing Andrew Ng's specialisation with Hands On Machine Learning by Aurélein Géron). But I also have the same problem, it takes me more time than what I think I should be investing, like around 1-3 chapters in 2-3 hours based on the length and complexity of the material. It might not seem like much, but I'm studying and preparing for another 4-5 topics rn, so I have little left for it, and I have to practice those concepts by applying them too. But I guess nothing will come out of worrying about it, I'll just have to build my speed over time.
Krish Naik 🐐
Do you know about Jeremy Howard?
Yup, I am familiar with him. He has a ton of great resources too!
I have found the best fundamental concepts in Machine Learning Specialization (Coursera by Andrew Ng)
Maching learning or any thing or programming it can be anything
I'm a beginner in machine learning so which projects should I do to get jobs.
Hi. I wanna learn basics about NLP between Neural Networks and Transformers architecture, for example tokenization, embeddings, RNN's etc. What do you recommend? Book, course (Coursera NLP spec. etc) or something else? Thanks
Karpathy's channel is great for this!
I've also created a bunch of free TH-cam videos on NLP concepts like tokenization, embeddings, RNNs, Transformers and more.
Check out this playlist: th-cam.com/play/PLf2BgkdQjMYvjsg0DMlMPspibTX6errDc.html&si=kMw4jZ8B9nG1qKG2
I am in 7th grade and I know the basics of Python and I have built some projects now I am switching to java and then c++ but how am I supposed to understand college level math and how can I still learn without needing extremely high level math till I catch up
Wow its great you are so young.
The thing is ML is built on mathematics. Its all mathematics in the end.
I tell you one thing, my friend is doing masters from top CS schools in my country, and they have rule that you can't take any ML course even if you are CS/AI graduate unless you are done with Linear Algebra, Vector Calculus and Random Process math courses.
So, highly suggest you to make Math(language of the universe) as you first love and take part in IMO and IOI selection process in your country and follow your ML passion side by side bit by bit.
Congratulations on getting such a head start! No worries if you're not at college level math yet, just take your time and trust the process, getting 1% better every day.
You can try Khan Academy videos, they have short, easy to understand explanations on most topics ( without going too deep into them).
What about Datacamp?
Haven't tried Datacamp - hopefully someone else can chime in!
What about datacamp
Haven't tried it - hoping someone else can weigh in on this!
Udemy 🔥
Which Udemy courses have you enjoyed?