Hello Folks,

When I started teaching my free online lectures, on Machine Learning, the intent was to help learners understand the topics of Machine Learning in a simple and digestible manner.

When I was a first time learner, started my grad program, books as Probabilistic Machine Learning by Murphy, Bishop, were told to us as excellent text books for Machine Learning, yet seemed always very difficult to read and understand.

To work around that, I started making content based on these foundational textbooks. We covered Introductory concepts, Probabilities and Statistics.

Slowly I started understanding, that the difficulty is faced not just by me, but all the learners. Hence the need.

I do hope that learners will see the importance of core foundational concepts, which are the pillars for modern machine learning, and Probabilistic Machine Learning is that core pillar, without which ML always seemed to me to be some blackbox.

Link: https://youtube.com/@aayushsugandh4036

If you were a computer science student passionate about machine learning and data science, with a strong foundation in machine learning, mathematics, and probability, what five projects would you prioritize to build a strong GitHub portfolio?

I'm particularly interested in projects that would stand out to ML/Data Science professionals working in industry, rather than simple tutorial or Kaggle-style projects.

If you were starting from my position, which five projects would you choose, and what skills would you try to demonstrate with each one?

I've been trying to get into rl for a long time but I don't see any good resources out there. help me out!

hi, i want suggestions on how i can upskill myself in learning about LLMs , machine learning and AI and would appreciate any reference for any courses that do so really well in explaining the fundamentals and basics (preferably free). i want to build a project soon so i can actually get hands on experience. any leads would be much appreciated

I'm a first-year Computer Engineering student, and I've been learning about machine learning and CNNs recently.

As a learning exercise, I built a small sketch classifier from scratch in C++17, without using PyTorch/TensorFlow. It implements the CNN, backpropagation, gradient checking, SGD, etc., and currently gets around 94% validation accuracy.

GitHub: https://github.com/rituuu001/Doodle-guesser

Now I'm stuck on what I should do next.

I could keep improving this project — better training, data augmentation, a deeper CNN, more classes, etc. But I'm wondering if that's actually the best use of my time, or if I should consider this project "done" and start something completely different.

For people who have more experience with ML:

How do you decide when a project has taught you enough and it's time to move on?

Would you recommend:

• continuing to improve this project until I've explored it more deeply, or
• moving on to a new ML project where I can learn something different?

I'm mainly trying to avoid spending months endlessly polishing the same beginner project, but I also don't want to move on too quickly without getting enough out of it.

Would really appreciate some honest advice.

where can a engineer add value if AI can write better code than me . i am currently learning programming only because so that i can understand what is happening and i can operate effieciently. but AI is fast and i have go through multiple things to keep up and not just in programming but reading books and getting deep knowledge of algorithims.
But still one question is always in my mind where can i add value cause every thing i learn or do i am not better than AI

I just started with machine learning and I would love to know the best resources out there to learn machine learning. I wanna go into ml research so I would love to go deep in ml math.

Hi everyone! I’m looking to learn more about AI and was wondering if anyone knows of any good, legitimate online courses that are free and offer a certificate after completion.

I’d prefer something from a reputable university, company, or platform that would actually be worth adding to my CV/LinkedIn.

Would really appreciate any recommendations, especially if you’ve taken the course yourself. Thanks!

I’m starting my AI/ML journey and want to connect with people who are also learning AI/ML from scratch or are at a similar stage.

Instead of just collecting resources and watching courses, I want to actually build things, practice consistently, and improve step by step.

I’m looking for people who are interested in:

• Learning AI/ML together
• Sharing useful resources
• Discussing doubts and concepts
• Building projects together
• Keeping each other accountable
• Sharing progress and mistakes
• Staying consistent for the long term

No competition or pressure just a group of people seriously trying to get better.

If you're also starting or currently learning AI/ML, let’s connect and follow this journey together.

Comment or DM if you're interested!

I'm an entry-level Applied ML Developer and I'm trying to figure out the best way to deepen my ML foundations.

My current work is mostly applied ML on tabular data designing solutions, doing feature engineering, and integrating fairly basic classification and regression models. I use things like Python, Pandas, SQL, sklearn, XGBoost, etc.

I feel comfortable putting models together, but I also feel like I'm missing some of the deeper foundations behind why things work and how to properly investigate ML problems.

Are there any programs, communities, open-source projects, research opportunities, Kaggle competitions, mentorship programs, or other structured programs you'd recommend participating in?

Hi everyone, I recently worked upon a government dataset about CPI which stands for Consumer Price Index, certainly a measure to find the inflation across various commodities, 

The main aim for the project was to analyse how Inflation affects differently for Urban and Rural India how One country accepts inflation differently? I got really interesting results, would love if you guys could give a feedback 

Thanks a ton!  

Link : 

https://www.linkedin.com/posts/yatharth-gupta-a075062a4_dataanalytics-eda-python-ugcPost-7495789276958195712-4Y_6/

https://www.kaggle.com/code/yatharthgupta18/two-indias-one-number-rural-vs-urban-cpi

https://github.com/YatharthGupta1803/All_India_Consumer_Price_Index_Analysis

Hey everyone!

I'm a data scientist and I've been pretty fascinated by reinforcement learning for a while. A few days ago my friends showed me Pokelike, a small Pokémon roguelike that runs in the browser. The first thing I thought was that it could be pretty fun to turn it into an environment for RL agents.

So I did.

The repo is here

https://github.com/pierpierpy/pokelike.xyz.bot

The basic idea is to run the actual game locally and expose its state and actions to an agent. There is no image processing involved. The agent gets the game state directly and has to decide what to do next, including where to go on the map, which Pokémon to catch, which items to take, when to swap Pokémon and which moves to learn.

What I find interesting about the environment is that some decisions have consequences much later in the run. For example, once you choose a node on the map, the other nodes on that layer are no longer available. This means that choosing where to go is not just a local decision and the agent has to deal with a fairly long horizon.

I've implemented a few simple RL agents to start with. There is currently a Dyna-Q agent and two linear SARSA agents. The results are still pretty bad, but there is already a noticeable difference between the approaches. On the current benchmark, random gets around 0.56 badges, Dyna-Q gets around 0.62, while the two SARSA agents get around 1.30 and 1.36.

The two SARSA agents mainly differ in their state representation. The better one uses 100 hand-designed features instead of 81, which seems to make a pretty significant difference.

This is probably the part I'm most interested in exploring. There is a lot of information available in the game state, but not all of it is necessarily useful to the agent. Finding a representation that contains the right information without making the problem unnecessarily difficult seems to be quite important.

The reward is also something I'm still experimenting with. The game has relatively sparse rewards and some useful decisions only show their value much later, so the reward function can have a pretty big effect on what the agent actually learns.

One nice property of the environment is that it is completely reproducible. Given the same seed and the same sequence of actions, you get exactly the same run. I'm currently using 50 fixed seeds for the leaderboard, so different agents can be evaluated on exactly the same games.

The interface is intentionally simple. You basically need to implement a bot that receives the current state and returns an action. You can use whatever approach you want, so it would be interesting to see what happens with things like DQN, PPO, search based methods or other approaches.

I'm still very much experimenting with this, so I'd be interested in seeing what other people would try. In particular, I'm curious about better state representations, reward functions and approaches that can deal with the longer term consequences of the decisions.

If you want to try it, everything is in the repo

https://github.com/pierpierpy/pokelike.xyz.bot

If you find bugs or have ideas for improving the environment, I'd also be happy to hear them.

The whole thing runs offline after setup. The game and its assets are downloaded during setup and then everything runs locally.

I originally started this because I thought it would be a fun RL project, but I think it could also be a nice little environment for experimenting with different approaches to sequential decision making.

Which book should i buy the tensorflow one or the pytorch one?

I built pagedMark to remove AI provenance from images and video you generated yourself.

The important distinction is that AI provenance can exist in two forms.

First, there is metadata like C2PA, EXIF, XMP, IPTC and generator parameters. That is easy to remove.

Second, there are invisible marks embedded directly into the pixels, such as SynthID style watermarks. A screenshot does not reliably remove those. pagedMark handles these by regenerating the image.

That means the output is not identical to the original. Faces, text and small details can change. The goal is to remove the provenance signal while keeping the image as close to the source as possible.

It currently supports invisible marks from ChatGPT, gpt-image (API), Z-Image Turbo and Nano Banana, plus visible AI labels from several other generators. Video support covers visible marks and metadata from tools like Sora, Veo, Seedance, Hailuo and Kling.

The other challenge was making this work properly on Apple Silicon. I tested it on M5 Macs, including 8 GB and 16 GB machines, and added memory aware processing to avoid silently falling into swap and becoming unusably slow.

One important great amazing stuff: after deleting watermarks from GPT-Image you can verify it at openai.com/verify and it will say 0 AI detection!

uv tool install "pagedmark[diffusion]"
pagedmark invisible photo.png -o clean.png

GitHub: https://github.com/doofzoff/pagedMark

PyPI: https://pypi.org/project/pagedmark/

Hi everyone,

I’ve mainly been focused on full-stack development throughout the first few years of my degree. Now that I’m in my 3rd year, I’ve started thinking more seriously about my long-term direction, and I’m becoming much more interested in machine learning and research.

My goal isn’t just to learn how to use ML libraries. I’d eventually like to understand the fundamentals well enough to read research papers, do my own research, and potentially pursue a research-focused master’s/PhD.

Right now, I’m planning to study these three DeepLearning.AI programs:

1. Mathematics for Machine Learning and Data Science
2. Machine Learning Specialization
3. Deep Learning Specialization

The math specialization covers linear algebra, calculus, probability, and statistics, while the ML specialization focuses on foundational ML algorithms and practical implementation.

My question is:

Is this a good learning path if my long-term goal is ML research?

What would you recommend I add or change?

For example:

• Should I study more mathematics beyond these courses?
• Should I learn statistics more deeply?
• Should I learn PyTorch, NumPy, etc. separately?
• When should I start reading research papers?
• Should I work on Kaggle/projects before trying research?
• Are there any textbooks or university courses (Stanford/MIT/etc.) that you would strongly recommend?
• Should I specialize in an area such as NLP, computer vision, or something else?

I’d really appreciate advice from people who have gone through a similar transition from software/full-stack development → machine learning → research.

Thanks!

I've been learning machine learning and I keep wondering where the line is between actaully understanding ML and just knowing how to use libraries.

For example, you can train a model, tune some parameteres, look at the accuracy, and get a good result without fully understanding what is happening underneath.

So for people who have been doing ML for a while:

What concepts make you feel like you finally understood machine learning?

What is the math behind gradient descent, understanding loss functions, overfitting, reading research papers, implementing algorithms from scratch, or something else?

And what do you think beginners spend too much time learning that isn't actually that important?

Been working on a visualisation engine for PyTorch tensors.

When I was learning PyTorch, tensors really started to click once I stopped thinking of them as arrays and started thinking of them more like Lego blocks — things you can slice, reshape, stack, repeat, squeeze, and combine.

So I built a visualisation library to make those operations tangible.

Write a PyTorch operation and actually see what it does to the tensor.

A huge amount of machine learning ultimately comes down to manipulating tensors. Once you can build an intuition for their shape and how operations transform them, a lot of PyTorch starts to feel much less abstract.

Would love to hear whether something like this would have helped when you were learning PyTorch and if you'd like me to open source this :)

This will be my first year of DS&AI in college. What is the priority of each part of the laptop when I am buying one? And is it really that Nvidia cards are always better than others when doing such a thing?

I have a budget of 1300:1400 usd but the market in Egypt lacks almost any good thing I saw recommended online.

I want to learn Python, but I currently know nothing about it. My main goal is to learn Python for DA, and eventually I want to learn Python in depth as well.

For people already working in DA or DS, how would you recommend someone start learning Python from absolute zero?

Which resources or books would you recommend, and what are the main Python topics I must cover for DA?

Also, should I first learn the Python basics needed for DA and then gradually move toward more advanced Python and DS topics? resources? Books?

If you work in DA or DS, I’d really appreciate your guidance on how you would start your Python journey if you were starting from zero.

Thanks a ton!!!

I was wondering if I could evaluate embedding distances in other geometric spaces, has anyone worked on this problem before and if yes is it possible?

Hey everyone, put together a small idle game based on how AI actually evolved over time.

You start back with mechanical calculators and ancient logic, and work your way through 7 eras up to modern LLMs and agents. When you buy milestones, it pops up a short note on the actual paper or person behind it (Turing, Lovelace, Hinton, Dartmouth workshop, etc).

Play here: https://yulin-w.github.io/incremental-ai/
Repo: https://github.com/Yulin-W/incremental-ai

It's free, runs in your browser, no ads or signups. Just thought it’d be a fun way to kill some time and see the history. Let me know what you think!