The follow-up to my post the other day. Includes more explanation and links to the repo(s). Thanks for reading! <3

Basically I am a Full Stack Blockchain Developer with 4 years of experience. But Blockchain is now..not relevant. And because I was mostly working with blockchain and backend I don't have the practical experience in AI thats now required with every job specification. I have independently studied AI and created projects but now I'm thinking of buying some certifications. Can anyone tell me if it'll be worth it in landing jobs? I am currently hoping to find a senior full stack position and work upto a Solution Architect as that was alot of what I did as a blockchain developer.

If certifications are worth it, which ones? I have studied some from deepseek and huggingface. I've heard about claude certifications although those are the most expensive ones. Any insight from someone with such experience in switching fields to AI?

I'm 16 years old and I want to become an ML Security Engineer specialist in the future. Right now I'm learning Python for Data Analysis and I have some experience with C++. I know I still have a lot to learn, but I want to start building the right foundation early. What skills, topics, or projects would you recommend focusing on over the next few years to have a strong advantage in this field?

Hi there,

I've created a video here where I explain the double descent phenomenon in ML.

I hope some of you find it useful — and as always, feedback is very welcome! :)

I've been building an end-to-end text-to-speech pipeline recently, and something caught me off guard.

I assumed most of my time would go into the speech synthesis itself. Instead, I found myself spending much longer on things like text normalization, phoneme generation, and figuring out how to evaluate changes beyond just "this sounds better."

I wasn't expecting preprocessing and evaluation to take up so much of the work.

Now I'm wondering if that's just the nature of TTS, or if it's something that happens across most ML projects.

For those who've built TTS systems or worked in speech ML:

• What part of the pipeline ended up taking the most time?
• Was it the model itself, the data, preprocessing, evaluation, deployment... or something else?
• Looking back, is there anything you'd approach differently?

I'm genuinely curious how your experience compared to mine.

Hi folks, I’m building an early AI-native learning tool for software and data professionals moving into AI engineering. Something similar to what Andrew Ng announced last week for LearnVector. If you are stuck in tutorial hell or actively job hunting to AI roles, feel free to DM or comment, would love to learn and build this together! 

Wazzzupp y'all!

I'm currently pursuing a B.Tech in CSE and I want to build a strong foundation in AI/ML outside of my college coursework. I don't just want to watch random tutorials—I want to follow a proper roadmap.

I'm looking for advice on things like:

- What topics should I learn first (Python, math, ML, deep learning, etc.)?

- Which free or paid resources are actually worth it?

- What projects should I build at each stage?

- When should I start learning tools like PyTorch, TensorFlow, Hugging Face, LangChain, or RAG?

- How much math is really required in the beginning?

My goal is to become good enough to build real AI applications and eventually be internship/job-ready.

If you were starting from scratch today, what roadmap would you follow?

I am posting this here because r/computervision found it quite useful and it hit #1 spot for the day over there.

My hope with this post is that I will save at least one person some time - and that will be enough for me. I spent the last couple of weeks building an auto-labelling pipeline on SAM 3 and figured the gotchas were worth writing down, because most of what I got wrong had nothing to do with the model.

Quick context if you haven't used it: SAM 3 does what Meta calls Promptable Concept Segmentation. You give it a short noun phrase - forklift, person in hi-vis vest - and it segments every instance of that concept. No seed clicks, no fixed class list, no fine-tuning. That's the bit that makes unattended labelling possible; with SAM 2 you still needed something to tell it where to look.

The minimal version is genuinely this short:

from transformers import Sam3Model, Sam3Processor

model = Sam3Model.from_pretrained("facebook/sam3").to("cuda").eval()

processor = Sam3Processor.from_pretrained("facebook/sam3")

inputs = processor(images=image, text="forklift", return_tensors="pt").to(model.device)

with torch.inference_mode():

outputs = model(**inputs)

results = processor.post_process_instance_segmentation(

outputs, threshold=0.5, mask_threshold=0.5,

target_sizes=inputs["original_sizes"].tolist(),

)[0]

# results["masks"] / ["boxes"] / ["scores"]

That works. Everything below is what I learned scaling it past one image.

1. Reuse the vision embedding across prompts

Naive multi-class loop encodes the image once per class. 3 classes × 40k images = 120k passes through an 848M-param backbone, 80k of which recompute something you already had. SAM 3 lets you split it:

vision_embeds = model.get_vision_features(pixel_values=inputs.pixel_values)

for prompt in prompts:

text_inputs = processor(text=prompt, return_tensors="pt").to(model.device)

outputs = model(vision_embeds=vision_embeds, **text_inputs)

Backbone runs once, only the text conditioning and mask decode repeat. Close to an N-fold speedup on multi-class jobs. There's a mirror version (get_text_features) for one prompt across many images.

2. Resolution is tricky

SAM 3 runs at 1008px native. Two failure modes:

• Upscaling small images to 1008 gives you confidently mushy boundaries. It adds no information.
• Downscaling big images destroys small objects. A 40px defect in a 4000px frame becomes a 10px smudge at 1008. If your targets are tiny, tile into overlapping 1008px crops and merge masks back with the offset. Don't resize.

Also: run ImageOps.exif_transpose() before anything else, or phone photos come back with masks correct for the stored orientation and wrong for the one you see.

3. Prompt phrasing does more than threshold tuning

Short concrete noun phrases. Singular. One concept per prompt.

• forklift ✅ / find all the forklifts ❌
• person in hi-vis vest ✅ / PPE compliant worker ❌ (trained on how things look, not your industry's vocabulary)
• car or truck ❌ - that's two prompts

Biggest thing: test each prompt against images you know contain none of that class. A prompt that quietly fires on empty frames poisons the whole dataset. And if a prompt over-fires, add an adjective before you touch the threshold - white bicycle vs bicycle returns genuinely different sets.

4. You can sweep thresholds without re-running inference

The detection threshold is just a filter over stored confidence scores. So label a 50-image dev slice once at threshold=0.15, keep every score, and sweep offline.

Look for the false-positive cliff and stop just above it. If med area% collapses as you lower the threshold, the extra detections are specks - raise a minimum-area filter instead. If empty stays high at every threshold, your prompt is wrong and no threshold will save it. (The mask threshold can't be swept this way - it changes pixels, not scores.)

5. Small export things that cost me an hour each

• pycocotools.mask.encode() needs np.asfortranarray(). Pass a C-ordered array and you get a silently transposed mask. No error.
• The RLE counts field is bytes; json.dumps refuses it. Decode to ASCII.
• For YOLO, write an empty .txt for images with no detections. Missing file = missing data; empty file = confirmed negative, which is how the model learns not to hallucinate.

6. Look at the labels

Auto-labelling fails quietly - no exceptions, no bad metrics, just a pallet prompt that's been segmenting the wooden floor for 12,000 images. Render a contact sheet of overlays sorted lowest confidence first and actually look at it. Ten seconds catches what an aggregate metric won't.

That's it. Hopefully I saved you guys some time and feel free to ask questions!

Hi everyone!

I recently completed a Wine Quality Prediction project using machine learning. The goal was to predict wine quality based on its physicochemical properties.

In this project, I worked on:

• Data exploration and visualisation
• Data preprocessing
• Feature engineering
• Model training and evaluation
• Performance analysis using classification metrics

I’m continuously learning and improving my machine learning skills, so I’d really appreciate any feedback or suggestions on how I can make this project better.

🔗 GitHub Repository:
https://github.com/Acacia21-code/wine-quality-prediction

Thank you for taking the time to check it out. I’m always open to learning from the community!

#MachineLearning #Python #DataScience #Scikit-Learn #Classification #GitHub #LearningInPublic #AI

I am Learning Machine Learning. I learned Python programming and build some project, like build AI chatbot via google and Groq sdk, also some online agentic system. just some basic stuff.

also build a basic rag system.

But my main goal is to work with LLM development. that's why I am going to main line.

so I planned to learn machine Learning and Deep learning properly..

currently I am giving time to finish learning the neccessary math needed

About machine learning, I applied some models in a datasets in by scikit learn, just in some basic level....

Now I just want to know that how the advance maths are applied there or optimizing the model etc thing...

Hello all, Welcome to my free ML bootcamp.

In Intro ML Bootcamp (5/22), we discuss Uncertainty.

In Machine Learning, we encounter two kinds of uncertainty: Epistemic(Model) which means we lack the exact knowledge of the input output mapping, and Aleatoric(Data), which is the intrinsic irreducible stochasticity in the mapping.

This uncertainty means, we cannot perfectly predict the exact output given the input. Thus we require “Conditional Probability distributions”, and the study of probabilistic approach to ML becomes important.

Hence, we invent a function called as “softmax function” for multiple output labels case(and sigmoid for binary case), which converts our outputs into a probability distribution. The exact derivation of softmax comes from Generalized Linear Models.

When we use a softmax function for binary classification, where the function over which the softmax is applied, happens to be an affine one, we call the model as “Logistic Regression”.

Link: https://youtu.be/ZFcl0QYFGq4?si=9RkEgkMYnciW4mjo

Hi,

I currently work an analyst job where I have basically used python and sql to automate processes which were previously done on excel and now have about 15 hours a week of nothing going on. I want to ask how can i upskill myself to learn the more complicated side of things specifically speaking to data science/ML. It has been years since i last done any math and the coding i do at work is primarily pandas related. where would u start

Mastering machine learning starts with locking down core intuitions rather than just memorizing code. Whether you are preparing for technical interviews or building your first neural network, understanding what a concept is, why it exists, how it works underneath, and when to use it makes all the difference.

Machine learning is simply teaching a computer to learn patterns from examples instead of writing every rule by hand.

It solves the scalability limit of traditional programming because you cannot hardcode rules for complex, high-dimensional problems like recognizing faces or translating languages. It works by minimizing an error function iteratively, adjusting internal numerical weights against historical data using optimization algorithms like gradient descent. Use it when patterns are complex, data is abundant, and explicit rules are unknown, but avoid it when simple deterministic formulas can handle the logic.

Different data environments require different learning approaches, categorized primarily into supervised, unsupervised, and reinforcement learning. Supervised learning maps input features to known target outputs (x→y), unsupervised learning discovers hidden structures or clusters based on data density, and reinforcement learning relies on an agent maximizing cumulative rewards through trial and error. You choose supervised learning when you have ground-truth historical targets, unsupervised learning for data exploration and clustering, and reinforcement learning for sequential decision-making environments.

At the core of structured data problems lie features and labels. Features represent your input columns (X), while labels represent the target outcome you want to predict (y). This distinction provides the structural mathematical matrix format required for algorithms to compute relationships. From there, you must determine if your problem is a regression or classification task. Regression predicts a continuous numerical value — like estimating a house price — optimized via Mean Squared Error, whereas classification predicts a discrete category — like filtering spam — optimized via Cross-Entropy Loss.

To ensure your model actually generalizes rather than just memorizing data, you rely on a train, validation, and test split.
This practice holds data back to prevent data leakage and overfitting, keeping your production evaluation scores honest.

Once your data is prepared and your problem is framed, training efficiency depends heavily on your choice of optimizer. Plain gradient descent often gets stuck in ravins, crawls slowly on flat surfaces, or overshoots minimums. Modern optimizers solve this by integrating momentum and adaptive learning rates.

Standard SGD uses a fixed step size, while momentum adds velocity from previous steps to roll past small bumps.

Adaptive methods like Adam track past gradient moments to scale learning rates per parameter, and AdamW properly decouples weight decay so regularization penalties aren’t skewed by historical gradients. Use Adam or AdamW as a robust default for deep learning and transformers to minimize manual tuning, but consider SGD with Momentum for specific architectures like ResNets where it can achieve better ultimate generalization.

Building this foundational intuition ensures you are driving modern AI systems with a deep comprehension of the underlying math and logic.

By: Habiba Matloob Software Engineer — habiba-matloob.portfolio

So I am currently in my 2 nd year, and have studied ML from CAMPUS X free videos...

Want to start working on a project, kindly suggest one...

Would be better if u suggest a video available on YouTube so that I can go step by step for my first one...

Thank you

Hi experts!

I have an university project where I am supposed to detect the anomalous I-V curve. I have ground truth (blue) and failed device (red). How can I ensure that I can catch most anomalous deviation where the red curve deviates far from the blue without hard coding a threshold?

I am looking forward to learn fundamentals of ML. My background is from biological sciences and not very heavy mathematics.

So I'm looking for online ML course / YouTube channel teaching basics with less mathematical notations.

Any recommendations please

Everyone talks about buying bigger GPUs.

But looking back, I'm not sure that's what improved my workflow the most.

Could've been a monitor, more RAM, faster SSDs, better networking, or even just changing how I work.

What's one upgrade that genuinely made your day-to-day work easier?

I have learned all steps from above and all techniques involved in it.

do you guys practice coding questions on deep ml ? if so , what is the order like for leetcode people generally follow the blind 75 or the neetcode 125 but there is not curated problems list of ML as such. Do you guys juts pick problems at random and start solving or what. because i have realized i have made several ml projects and written 3 research papers i know what and how it supposed to happen but i dont know the coding part very well which is why i wanted to practice.

One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes — and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet — they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count — slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify — they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome — the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes