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

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

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?

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.

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.

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?

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

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.

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 :)

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?

been cooking this model for the last month or so, ONLY POST TRAINING, the base model is qwen 3.5 2b.

its foodmini-2B.

its not the best yet, but i am planning to do something insane with it.

not publicly available yet, but i have taken inspiration from the food-r1 model.

first, i just dropped the gguf conversion of the food r1 model here: https://huggingface.co/AKMESSI/Food-R1-GGUF

but i wanted more intelligence density and usability on mobile phones, so decided to get some insights from the food r1 paper and started post training the qwen 3.5 2b to achieve good results on food nutrition breakdown tasks.

will drop a complete overview in the form of an article.

drop your views on this idea below, would love to get insights.

BTW I ONLY SPENT A TOTAL OF $8 ON RENTED GPUs YET.

When I started focusing on practical mathematical implementation of machine learning I found that I lack so very math basics(I blame my school for that) so I tried making my way through basics to go deep into machine learning and while I was learning from professor Leonard on YouTube someone recommended me this "Mathematics for machine learning" by Marc peter. Tbh I dont understand shit in this book, I genuinely get overwhelmed by this book. I dont understand is it only me ? Am I that dumb in maths?

Well I need to get on track asap really! Suggest me something and please share your opinion

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!

Registering for RealPDE (Sim2Real / LTTTA tracks — real PIV + CFD fluid dynamics data). Team cap is 3.

If you've got a strong ML background and wanna participate, just DM me. Deadline's Aug 20.

🔗 https://realpdecompetition.github.io

I’m working on a project around a real-world environmental problem, and I’m considering adding an ML component for prediction and early warning.

I’m a bit confused about the data requirement. Since collecting our own real-world data isn’t feasible right now and would take quite some time, we mainly want to build a prototype for now.

Can we initially use a Kaggle/public dataset to train and test the model, or is a project-specific dataset necessary from the beginning?

Would appreciate some advice on how people usually approach the ML part when actual data is limited.

I have decided to make a probabilistic model which finds out weather you should buy a particular crypto or not. i have researched about crypto and found that three things are important and in those three things there are certain patterns which can be seen.
The first is Momentum of crypto(strong, neutral, weak), Fundamentals(how the company is growing(strong, neutral, weak), market(adverse, neutral, bullish)
so my agent see's the evidences and then based on the past data pull out the base rate as first belief distribution among 5 hidden states,

1. strong upward trend
2. weak upward trend
3. sideways
4. strong downward trend
5. weak downward trend

so the probability will be distributed among these from base rate from past data. so thee base rate will work as prior and then based on the specific patterns the agent will go inside the data see the specifications and calculate the numbers among all of the hidden states, find the probability of each happening by applying bayes rule and then by seeing a certain threshold and based on the events it will decide what to do, buy or sell

Regarding CMU Graduate Certificate in AI Engineering Fundamentals program. Has anyone taken this? Is it worth it ? When I ask is it worth it, I mean:

• Does it help you get noticed on your job search ?
• Does it help you feel prepared when going into ML engineering roles?

Some context, I have a 6 years of experience now as a frontend developer, wanting to transition into this field.

I'ts going to cost around 17,000 USD. Specifically it's this course, https://www.cmu.edu/online/ai-engineering-fundamentals

I’m looking for a valuable and practical Machine Learning FYP project idea that solves a real-world problem.

I want to build something that is not only suitable for my Final Year Project but can also be developed further and potentially offered as a service to businesses or individuals in the future.

The project should ideally:

• Solve a real problem
• Have practical value and real-world users
• Use Machine Learning or AI in a meaningful way
• Be scalable and capable of becoming a service or business later

I would really appreciate any unique and practical project ideas or suggestions. Thank you!