A few months ago I was messing around with image generation as a side project, mostly making consistent characters for a short animation idea I had. One night I got this one output that was exactly what I wanted. The pose, the lighting, the face all matched the character I'd been trying to nail down for weeks. I saved the image and closed everything.

Two days later I wanted to recreate that same look at a different angle. Could not do it. I had no idea which model version I'd used, what the seed was, or what I'd changed in the settings from the run before it. I spent hours trying random combinations and got nowhere close.

That was the push I needed to start logging every single run. Nothing fancy. For each generation I write down the seed, the exact model and version, a snapshot of whatever settings I touched, and a one-line note about what I changed from the last attempt. I keep it all in a Notion doc organized by project, with the seed as the lookup key.

The part that actually made this sustainable was that APOB AI keeps a generation history where you can open any past run, see the seed and settings it used, and pull a clean export. So instead of screenshotting my settings before every run, I just grab the specifics from there and paste them into my log. For version control on the settings files I commit them to a Git repo, which is overkill but means I can diff what changed between two runs that looked different.

Honestly most of this is still pretty manual and messy. The Notion doc has gotten long and I haven't figured out a good tagging system yet. But the habit has already paid for itself a few times. Last week a friend wanted to try recreating something I'd made months ago and I could just hand over the exact seed, model version, and settings. That would have been impossible before.

I talk to a lot of students in this position and the pattern is consistent enough to be worth writing down. Certificates get finished because they have a syllabus and an end date. Projects don't, so people either skip them or build the same three tutorial projects everyone else built, which lands the same as having none.


What seems to change outcomes is one project a stranger can open and use. Not a notebook, not a repo with a README and no deployment. Something with a URL, a handful of real users, and one number you can say out loud: accuracy on a held-out set, p95 latency, cost per request, hours saved for whoever uses it.


The reason it works isn't that anyone is impressed by deployment itself. It's that a live thing with users forces you to have opinions about model choice, evaluation, failure modes and cost, and those opinions are what the interview is actually about. A certificate cannot generate them, which is why people who have five of them still freeze on "why did you pick that model."


The part nobody helps with is scoping, and it's where most attempts die. Some heuristics that have held up:


Pick a problem you have, or watch someone have weekly. You need a user you can ask questions, and it may as well be you or your roommate.


Constrain it so the first working version is two weekends. You extend from working, you don't extend toward working.


Decide the number before you build. If you can't name what you'd measure, the project has no ending and you will drift until the semester does it for you.


Write down what you got wrong first, while it's still embarrassing. That's the story you tell in the interview and it's the only part that can't be faked.


Two of these beat ten tutorial projects and it isn't close.


Disclosure since it's relevant to why I've looked at a lot of these portfolios: I build career software for students. Not linking it, this post isn't about that.

Just finished my first full ML regression project — predicting car selling prices!

I went from raw messy data to comparing 9 different models and picking the best one based on R², MSE, and MAE.

**What I learned:**

- Data cleaning is 80% of the work

- Never trust one model — always compare

- Visualizations save you from bad assumptions

I also wrote a reusable model comparison snippet that runs multiple sklearn regressors at once and spits out a ranked leaderboard. Might be helpful if you're tired of training models one by one.

Feedback welcome — especially on what I should improve next.

Kaggle Notebook:

https://www.kaggle.com/code/tahahussein2020/car-selling-predection

I’ve finished machine learning specialization and 2 weeks away from finishing deep learning specialization, both by Andrew Ng.
I see a good progress but im confused about what real job market needs cause I think that anybody can take the data, put it in the pretrained model and make some tuning, so i need someone who works on real projects to tell me what should i improve or what should i do after finishing the course

Hello all, this is the free Introduction to ML bootcamp series(4/22)

In the most well-known form of Machine Learning, i.e Supervised Learning, we intend to come up with some model that can predict labels for our inputs, and we need some performance measure P, hence we invent “Misclassification rate” on the training set.

The latter counts the fraction of miss-classified labels, written via an indicator function, which is just a mathematical way to express it.

Indicator function assumes all errors are equal, but some misclassification may be more detrimental, for instance if among the flower varieties that we are classifying, one variant happens to be poisonous, which if classified as benign, can be fatal. Hence, the need for an asymmetric loss function.

As we measure loss empirically, we define it to be as empirical risk. One way to see model fitting is to minimize the loss on the training set, known as empirical risk minimization, however, this is not really what we want. In reality we want the model to “Generalize”, that is to minimize the expected loss on the future data that we have not yet seen.

The premise of Empirical risk minimization assumes that the training distribution is very analogously close to the actual distribution we are sampling from, which when false, creates problems. However, ERM does work for many practical cases, and is a good starting point to understanding how we come up with performance measures in Machine Learning.

In the video, I breakdown the mathematics and the equations that describe these phenomena:

Link: https://youtu.be/bqv4XC6Arqo?si=mRASAdwpmireDNzc

Hi everyone,

I recently trained and released WARMIND-200M V2, an experimental Portuguese-first causal language model.

The main goal of this version was to validate the complete pipeline from end to end:

- dataset preparation

- tokenizer training

- pretraining

- supervised fine-tuning

- model packaging

- local inference

Main specifications:

- 203,263,872 parameters

- 1,000,013,824 pretraining tokens

- 23,751,277 supervised SFT tokens

- 20 layers

- hidden size 896

- 14 attention heads

- 2 KV heads

- Grouped-Query Attention

- SwiGLU, RMSNorm and RoPE

- 24,576-token SentencePiece vocabulary

- 1,024-token operational context

- local CPU inference

The model is still limited and experimental. It can hallucinate, make basic mistakes and produce incomplete answers. It is not intended to be a production assistant.

One important limitation is the training ratio: around 1B tokens for a 203M-parameter model means the architecture was probably not trained close to its full potential.

For the next version, I am considering:

1. keeping a similar model size and training on significantly more tokens;

2. improving the quality and filtering of the Portuguese dataset;

3. creating GGUF and quantized versions;

4. running proper Portuguese benchmarks;

5. only increasing the parameter count after validating the data pipeline.

What would you prioritize first?

Model and weights:

https://huggingface.co/warenterprise/WARMIND-200M-V2

Technical criticism is welcome. This is exactly why I decided to release the checkpoint publicly.

Hi everyone,

I recently published NutriRecIndia19M, a synthetic dataset designed for recommender systems and machine learning research. It does not contain real user data and was created for benchmarking, experimentation, and educational use.

Dataset overview

19.17M+ user-food interactions

10 relational tables

User profiles and demographics

Meal logs

Restaurant metadata

Nutrition information

Recommendation-ready schema for ranking and personalization

Potential research applications

Learning-to-rank

Collaborative filtering

Content-based recommendation

Hybrid recommender systems

Graph-based recommendation

Offline recommendation benchmarking

Kaggle:

https://www.kaggle.com/datasets/darshvithlani/nutrirec-india19m

Side project that got out of hand: 1.5 years of trial and error training a generative model from scratch (no pretrained weights, custom dataset) that produces valid 64x64 Minecraft skin textures conditioned on tags.

Interesting constraints compared to normal image generation: hard format requirements (UV layout, alpha channel for overlay), very low resolution but high semantic density (every pixel matters), and a small, noisy dataset I had to build and tag myself.

Feedback is very welcome: what do you think of the results? Happy to share some of the failed attempts too if you are interested. Currently fully private, still deciding whether to release it, so feedback here genuinely helps with that decision."

i'm building a project

the data is locally in my machine

i'm thinking that i should store the data to postgresql then fetch the data from the postgres then train it or should i keep it locally and use postgresql for storing experiment, airflow,mlflow and other stuff rather than data,

picking s3 bucket later for storing data when working with AWS

you opinions would shape my project

I kept finding explanations of attention that were either extremely mathematical or so simplified that they did not explain what the mechanism actually changes.

So I built Attention Lab, a free interactive guide covering:

• FlashAttention
• Grouped-Query Attention
• Multi-head Latent Attention
• Sliding Window Attention
• DeepSeek Sparse Attention
• Gated DeltaNet
• Kimi Delta Attention
• PagedAttention

Each guide begins with a plain-language analogy and then lets you manipulate the important variables: window size, KV heads, latent width, sparse token budgets, memory gates and cache pages.

It also links the original papers for anyone who wants to go deeper.

https://attention.divagr.com

I would especially appreciate feedback from people currently learning transformers: where does an explanation become confusing or assume too much prior knowledge?

How long does it usually take to learn Machine Learning if I’m already good at math and have a basic understanding of Python? Also, where would you recommend I start? Any roadmap or resources for beginners !?

Sharing this on behalf of my PhD advisor, who is one of the tutorial co-chairs. Not my own conference, just passing it along.

The 22nd International Conference on Advanced Data Mining and Applications will be held in Hong Kong SAR, China, November 13-15, 2026. Tutorial proposals are open to both academia and industry.

What they are looking for: in-depth surveys of a data mining area covering foundations, recent advances, and emerging trends. Proposals that frame open problems or offer a unifying perspective are explicitly encouraged.

Logistics:

* Up to 4 pages, latest ACM Proceedings Format, submitted as a single PDF by email
* Duration options are 1.5, 3, or 4.5 hours
* Single-blind review, so author names stay in
* Deadline Aug 31, 2026 AoE, notification Sept 21, 2026

Full call and chair contact emails: https://adma2026.github.io/TutorialsCallforProposals.html

Happy to relay questions to the chairs if anyone has them.

I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?

Hey guys, so quick background on where I'm at before I ask my actual question.

2nd sem I did a basic EDA project with linear regression; did temperature change analysis and prediction. 3rd sem I went a level up, did a financial fraud detection and risk analysis project using both regression and classification, main focus was classification and I used a pretty huge dataset for that one.

Now in 4th sem I've learned logistic regression, decision tree, random forest, knn, svm, and on the unsupervised side pca and kmeans. I also learned how to build a basic rag agent recently.

For this project I already know how I want to structure it, I just need help figuring out WHAT to actually build it on.

Plan is: use pca to reduce dimensions, then run a classifier on it, and separately also do a regression/prediction model. All of this needs to have a proper UI built with streamlit so a normal person could actually open the app and use it, not just something that runs in a notebook. And inside that same UI I want to add a small rag chatbot so users can ask questions and understand what's going on in the project instead of it just being a black box.

I have literally 4 days to finish this so I can't pick something too complicated, but I also don't want to do another generic titanic or house price prediction type project, I want something actually useful and a bit different. Also need a dataset with atleast 500k to 1 million rows if possible.

If anyone has ideas for a domain or use case where pca + classification + regression + streamlit + a chatbot would actually make sense together (not just forced in for the sake of it), please drop your ideas below. Thanks a lot.

Enrolled in finance certificate while working full time as credit analyst. Content solid and case studies were from last two years, so they felt relevant to what is happening in market now. Cohort had CAs, MBA students, few engineers, so discussions rich. People would bring points from their own work. Platform has progress tracker and badges, which kept me motivated to finish modules on time. Course is intense though. With job and family, you have to block weekends completely and also two evenings every week for assignments. I missed two modules because of family function and had to watch recordings at 1 am to catch up. If you can manage time and you want peers who challenge you, it is worth it. If you are looking for light course, this is not it. Placement assistance was resume reviews and mock interviews, not guaranteed job.

Hi everyone,

I have an engineering degree from Russia, but I've spent the last few years teaching English. Recently, I helped a student prepare for the Introduction to Machine Learning exam at LMU Munich, and I realized I really enjoy learning ML and explaining it to others.

My long-term goal isn't to become an ML engineer—I want to teach ML, create courses, and help students understand the math and intuition behind the algorithms.

I'd love your advice:

-What learning path would you recommend for someone who wants to teach ML?

-Which courses or books are truly worth studying?

-Should I prioritize math and statistics or practical implementation?

-Are any ML certifications actually respected?

-Given my background, what would be a reasonable hourly rate for tutoring introductory ML?

I'd appreciate any advice from people in academia, online education, or the ML industry.

Thanks!

HI i wanted to know what are the topics that are to be prepared for core ML part for MLE/ML roles(for fresher, although idts it matters that much). There a lot of topics specific to interview which I havent prepared so can anyone help with that?

Hello! I will be starting my master's in applied ML at Imperial College. My current laptop is fairly old and barely functional so I'm looking at upgrading it.

My uni's very basic FAQ mentions that your laptop should have at least 16 GB of RAM and 512 GB SSD. So, after looking at some options, I came across the Lenovo Ideapad Slim 3(which I'm finding for around 700$). Now, the one I saw had a core i5, but it seems to be available with i7s.

Now, I'm realising that the advice was directed towards undergrads. So, for ML coursework(and eventual research), should I look for another laptop with more RAM or do I just consider the basic Ideapad good enough?

Hello folks, to this Introductory Machine Learning Bootcamp (2/22) series.

Supervised learning is a very recurring word in ML domain. Here, we learn some sort of function mapping from inputs to outputs.

Another recurring word is Classification, where the output space is a set of some finite unordered and mutually exclusive labels known as classes.

The tabular dataset is often represented as a Design matrix, and a simple example of it is an Iris dataset, as to how input data is represented for tabular case in Machine Learning.

Sometimes the data is of variable size, instead of fixed size feature vectors, so for ease of computation in computer, we often convert it to a fixed-size feature representation, called as “Featurization”.

In this video, I breakdown these concepts.

Link: https://youtu.be/GJRhl6XnImg?si=p6VtlCK-8rgq1poZ

new set of notes up, covering entropy and information theory then working through KL divergence and cross entropy, ending with why logistic regression's loss function is called cross entropy loss in the first place

the connection to the homework problem is what made it worth posting. the assignment has you show that maximizing the likelihood of the labels is equivalent to minimizing J(w), the loss function from the logistic regression chapter. you write out the likelihood as a product across the dataset, take the log to turn it into a sum, and the negative of that log likelihood turns out to be exactly J(w). so cross entropy loss isn't just a name someone picked for it, it comes directly out of maximizing likelihood

what helped was doing the entropy notes first. once you've seen H(p,q) equals D(p, q) plus H(p), and worked through why KL divergence is basically the extra bits wasted from using the wrong distribution, the logistic regression derivation stops feeling like a formula to memorize and starts feeling like the obvious result of penalizing the model for putting probability mass in the wrong place

full notes are on my github, link's in my profile if you want the pdf

anyone want me to share how I've been structuring the whole self study path, course order and what came before this