วีดีโอ

What ML algorithm would you recommend for a RuneScape PvP bot? I was trying to use qlearning bc it was easy to get started, but the state action space feels too small to develop a useful pking bot.

There will always a Chinese dude who is better than you and has more research papers that will get the job first. Welcome to the job hunt hell.

🐐

Do you have a discord server where your community can chat?

I saw your , website and your paid courses, But how are your courses is different from other courses on udemy?

ML 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): bit.ly/3Zgwknf Beginner's Blueprint (Build Projects): bit.ly/3UYptw4

Nice

I really love your videos Bro

I'm a beginner in machine learning so which projects should I do to get jobs.

For a beginner which site would u recommend for ML papers(I'm in bachelors and learning the maths behind the ML algos)...also mentioning the names of some paper to start with will be helpful. I'm thinking of word2vec ?

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.

Don't you think you got a little bit repetitive. I mean you've always mentioning gradient descent. I know it's important but i mean maybe explain ViT someday or i don't know

I have found the best fundamental concepts in Machine Learning Specialization (Coursera by Andrew Ng)

Can you make a whole roadmap video in detail like start with python go to pandas then numpy so on also mentioned where did you learn this as self-taught coz I am the one.... Also I feel like I am learning not enough and fast recently

Krish Naik 🐐

Great video! I was wondering if you can respond back to the email I sent you? Thanks a lot!

More 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): bit.ly/4hZyK0H Beginner's Blueprint (Build Projects): bit.ly/3YX0Xwv

its 4am and i just woke up and i seeing this magical video that was very helpful thanks <3

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

What about Datacamp?

Your community will definitely grow mate! Keep up the work!

What about datacamp

bro i emailed you you didnt respond

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

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

Do you know about Jeremy Howard?

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.

Maching learning or any thing or programming it can be anything

Udemy 🔥

I love the short and straight to the point sub 5 minute style! Keep up the awesome work! I like to watch them :)

More 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): bit.ly/40XVVCO Beginner's Blueprint (Build Projects): bit.ly/4fAdEoh

More 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): bit.ly/412TTku Beginner's Blueprint (Build Projects): bit.ly/4eCE3An

Can someone recommend where to read those papers of research on ml/data science

I mean, three minutes is technically less than 5 minutes

I know it’s a lot to ask for. Can you please start a series wherein you select one ML paper and explain that. Maybe 1 paper in 2-3 weeks. This would be immensely helpful in understanding how to read these papers, extract relevant details and replicate it in PyTorch with proper project structure. Atleast maybe do this for 1 ML paper completely for free.

1.Apply for internship position at your university. 2.Learn Gradient Descent and linear regression 3.Apply to smaller firms because there requirements are not strict as compared to big tech companies 4.Learn leetcode and system design 5.Add projects to your resume

Hey man I just wanted to say to keep up the videos they are great. Also is there any way to get in contact with you?

Thank you 👍, I've just started and these tips are really good ( which I wouldn't have realised by myself even later on)

Hey , thanks for the info! , just a quick note , can you plz replace the stock footage with anything else ?, There are so many of them and they are quite distracting

What are the advices for people who cant afford to pay a university and are learning on their own.

Yoooo, congrats on your graduation! What was your bachelor's?

My advice to everyone don't learn ml or data science for getting a internship you will regret it they are verry low or no internship for fresher in that field even entry level jobs required 2 year of experience instead learn any other suff. And then apply ml or data science on that stuff And most important ml requires lot of math so ready yourself of intergals

The amount of information you give out for free on this channel is goated. We all appreciate your content, Dev

Hey thanks for this video! Some constructive criticism - I kinda find the stock footage to be a bit distracting, I would rather prefer a sped up footage of you coding or some other ML or Computer Science related thing.

I agree with Leetcode and System Design part. I am currently hunting for System Design resources from what i have found System Design Interview books (volume 1,2) from Alex Xu, Designing Data Intensive applications are the best resources.

Great Video. Thank you

What I like about ML more than SWE is that I don't bother thinking about a new project, I just go through some big deep learning paper and I try to reimplement it from scratch in pytorch. The list can be very long varying from detection or segmentation to text translation or sentiment analysis. It's litteraly an unlimited source of projects.

Image is 16*16 is a brilliant paper.

I love the way you explain things. That's fast, concise, and clear. Keep it up!❤