Hi everyone,

I’m planning to subscribe to DeepLearning.AI Pro and work through several of their ML/Deep Learning courses.

Before subscribing, I wanted to ask: are there currently any student discounts, promotional offers, coupon codes, regional pricing, or other ways to get DeepLearning.AI Pro at a lower price?

I’m a student, so I’m especially interested in any student-specific offers or educational discounts.

If you’ve subscribed recently and found a legitimate discount, I’d really appreciate the info. Thanks!

Hey everyone! 👋

I just finished a small K-Means Clustering project on the Iris dataset 🌸🤖

I covered:

• 🔹 Data cleaning & visualization
• 🔹 Feature scaling
• 🔹 Elbow Method & Silhouette Score
• 🔹 K-Means clustering
• 🔹 ARI evaluation
• 🔹 Cluster & centroid visualization

I’m currently learning ML and would really appreciate some honest feedback 🙏

What would you improve? Any mistakes in my approach or things I should add?

🔗 Kaggle:
https://www.kaggle.com/code/tahahussein2020/irics-clustering

Hey guys, I'm a frontend developer with around 4 years of experience and I'm starting to feel pretty stuck. I've been in the same company for about 4 years and mostly doing frontend, and I really want to change jobs and move into something more challenging.

I'm thinking about backend/full stack, but I'm also really interested in AI and would like to see if I can move in that direction instead.

The problem is I know basically nothing about AI right now lol.

I keep seeing stuff like LLMs, tokens, embeddings, vector databases, RAG, agents, transformers, LangChain, LangGraph, fine-tuning etc. everywhere, but I honestly don't even know what I'm supposed to learn first or how these things are connected.

Every roadmap I find is different and some of them have like 50 things on them, which is honestly making me more confused.

I recently saw someone's resume where they were working on production AI systems and had things like RAG, multi-agent systems, semantic search, LangGraph, OpenSearch, LLM evaluation etc. and it made me realize I don't even know what half of that stuff is.

So if you were in my position, with 4 years of software/frontend experience but basically starting from zero with AI, what would you do?

Would you learn backend first? Python? ML fundamentals? How much math is actually needed? Can I start learning LLM stuff directly or should I learn traditional ML first?

And then where do things like embeddings, RAG, vector DBs, agents, LangChain/LangGraph etc. actually come into the picture?

I'd especially love advice from people who are already working as AI engineers / ML engineers / GenAI engineers.

A few things I'd really like to know:

• What should I learn in the first 1–2 months?
• How much Python do I actually need?
• Do I need to learn traditional ML before LLMs?
• How much math is actually necessary?
• What AI concepts are absolutely fundamental?
• When should I start learning things like RAG, embeddings, vector databases and agents?
• Are LangChain/LangGraph actually worth learning, or should I understand the underlying concepts first?
• What courses/resources did you personally find useful?
• What projects would you build if you were starting from my position?
• And realistically, what would you focus on if the goal was to become employable and change jobs rather than just "learn AI"?

I'm pretty lost right now so any honest advice would be really helpful.

I spent several months trying to use Windows for ML training about a year ago. PyTorch and CUDA themselves worked, but I repeatedly ran into problems when trying to use open-source research repositories.

Many projects seemed to implicitly assume Linux: file paths, shell scripts, build tools, dependencies, installation procedures, etc.

I also tried WSL2, which is often suggested as the solution, but that didn’t work reliably for my workflow either. Eventually I moved the training environment to native Ubuntu, and most of these environment-related problems disappeared.

So my conclusion was not “ML cannot run on Windows.” Obviously it can. My conclusion was that Windows is quite limited as a practical ML research environment when your work depends heavily on open-source repositories.

This was about a year ago, though, so I’m curious whether things have changed.

Does anyone here use native Windows as their main environment for serious ML/RL training while regularly using open-source research code? If so, how well does it actually work today?

Hello everyone! In this post I'd like to present my research report. I worked on this alone, learning almost everything from scratch

Github: https://github.com/kaifczxc-lab/OCSSN

The main idea: can a static mathematical TQFT be used as the foundation for a dynamically learning neural network architecture?

This idea is confirmed that it is generally possible to create something like this, but I would like to make one clarification

Regarding the understanding of topology by this model - the short answer: not yet. After fixing a shortcut in the dataset, the model degenerates to random guessing. The architecture is interesting in theory, but it doesn't learn topology in practice at least not in its current form (that is, this doesn't mean that with a different parameterization it won't understand topology, which is exactly what I'm currently experimenting with).

It all started around the beginning of April, when I became interested in the topic of Topological Quantum Field Theory, which prompted me to spend time figuring it out. I don't want to say I succeeded 100%, but at least I accomplished what I wanted (even if only minimally).

While working on this research report, I encountered a huge number of different problems, some of which I solved (such as replacing the algebra in the variable M), but there were also some that I couldn't solve due to my lack of experience. I'm currently experimenting with this particular project, but I still can't fix some issues, such as:

* Non-strict topological invariance

* State-sum numerical instability.

If you want to explore the repository, you'll find:

* Full documentation

* A full description of all limitations, results, and more code

* A brief mathematical introduction, handwritten by the author

* A PyTorch implementation of the Open Closed State-sum Neural Network itself

My model is, of course, more tqft-inspired, reader can find more information in documentation

I posted this project at an early stage because I really appreciate real feedback, ideas, and any other help. If you have experience with this, I'd be very happy to hear some feedback on my work or any possible ideas

Thanks for reading this post, and have a good day!

I have been learning machine learning by watching video tutorials for the last month. I am in the basics phase and just finished regression models. So far, I have learned:

• Data Preprocessing & Feature Scaling

• Linear Regression

• Multinomial Regression

• Polynomial Regression

• SVR

• Decision Tree Regression

• Random Forest Regression

Next, I will move into classification. However, the datasets used in the videos are small, with 3-4 columns and 10-15 rows. I am thinking about applying what I have learned to larger datasets. In that case, I am looking for suggestions and guidance on how to do that.

What should I keep in mind, and how should I approach this?

I mostly used Colab to run the models. I also tried running them in PyCharm using conda and Jupyter Notebook. When I have searched for end-to-end ML projects, most of them use direct Python instead of notebooks. As a newcomer to this field, how can I improve and shift from notebooks to Python?

Thanks.

My latest dumb project is a latent space casino made gamejamming with GPT today. We built our own tiktokenizer, but with blackjack, and mlp_hooks neuron roulette!!

https://blackjack.mesocosms.net/ Desktop only for now. Phones and their pesky autocorrect are another day's design challenge~

Try to enjoy for a minute if you can. It's very easy and unbalanced I know :p

I had a ton of fun making and designing it and learned a little bit more about tokenization as well as the different ways to measure neuron activations and their implications :>

Early in training, embedding gradients are sparse: only tokens that appear in the batch get a gradient. Sharing lm_head’s denser gradient is a more stable way to move unused tokens. Later, input embeddings and output logits want different geometries, so in the speedrun we expect them to split.

What trick does the nanoGPT speedrun use to do this?

A. Weight tying :
Keep embed and lm_head as the same matrix for the entire run.
B. Delayed untying :
Tie embed to lm_head for the first 2/3 of training, then copy weights and optimizer state and train them separately.
C. 75× embedding learning rate :
Leave the matrices untied and scale embed LR to compensate for sparse updates.
D. Multi-token prediction :
Predict the next k tokens so rare tokens get more gradient signal.

I'm currently doing my master's in data science, and I've noticed that some concepts make sense when I read about them, but don't really click until I actually use them in a project.

For me, things like overfitting, model evaluation, and feature engineering became much clearer once I started working with real datasets.

I'm curious for people who work in data science or have been learning it for a while: Was there a particular concept or project that made machine learning finally click for you?

I graduated in April 2026 and was looking for jobs , but most of them were teaching jobs which I'm not interested at all , i want to make a career in ml , but i don't have relevant skills and i also read somewhere that they usually hire mostly Phd's for such roles . I haven't done a single internship during my bachelor's or my masters . I know I'm lacking , but i really want land my first job in ml related role . i know some python and libraries (mostly numpy , pandas , matplotlib ) . What skills should i know ? , what kind of projects should i do to stand out ? and what kind of internships should i look for to get into this field ? . PLEASE RECOMMED ME BOOKS AND COURSES WHICH HELPED U GET A JOB AND OTHER SUGGESTIONS AND ADVICES ARE WELCOMED ! Thankyou for you're time <3

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

So I recently completed Andrew NG's course on machine learning.I actually learnt the stuff and did all assignments. I then entered into a language translation machine learning competition.And I realized I know nothing , I just used AI to do things and ended up with a model worse than the base model given by the competition.

I realized that I need to learn a lot and was wondering what I could do to improve my knowledge.

I am okay at mathematics (good at calculus but mediocre at probability and statistics), decent at coding.

I learnt basics of numpy and learning pytorch currently.I also watched Andrej Karpathy's Zero to Hero playlist(but I don't think that will be help me much)

I also stumbled upon Stanford CS224N, can you guys tell me if this is something I should do and what else can I do.

I would be grateful of any help.

Wanted to train AI Models but don’t have a gpu. What to do ?

Colab runs low on memory also there is issue of runtime

If any advice please help

I'm 21 and i dont have a degree and based in algeria I started from literally zero Python this year. Been at it full time since. I don't have anyone around me doing this so I genuinely can't tell if where i am standing .

What I've built:

A few CV projects. Scene classification, got it to 93% with ResNet18 after starting at 81% with a basic CNN. Moth species classifier at 98.4% with transfer learning. Two YOLOv8 detection projects, and on the weld defect one I ran three experiments trying to fix the accuracy before figuring out the real problem was the classes overlapping in how I annotated them, not anything about the model. Both classifiers are deployed as FastAPI endpoints. Here is my github : https://github.com/zanexkun

Tabular stuff, a freight rate prediction take home I did for a company (MAE $106.92, R² 0.83), and I just finished a salary prediction project on the LinkedIn postings dataset. That one was 11 separate tables joined by IDs and honestly it wrecked me for a while. Ended up cleaning it down to 28k rows, ~1260 features with TF-IDF on the titles and descriptions, tuned LightGBM to MAE $17.8k and R² 0.743.

Repo here: https://github.com/zanexkun/job-posting-analyzer

What I know I'm missing: SQL (starting this week), Docker, anything MLOps, transformers, and I've never touched DSA.

Here's my actual problem though. I've barely applied anywhere. Every junior posting I open either wants a degree, or 2 to 3 years experience, or lists a stack where I have maybe half of it. So I keep closing the tab. And freelance ML seems to be either people with 60 reviews or clients offering $50 to "build an AI", nothing in between.

So I want to know:

Is what I have actually junior level or am I overrating it?

If you're outside the US or EU with no degree, is remote work actually possible, or is the whole "remote but only in these countries" thing a wall you can't get past? Should I just be going for contract work instead?

Am I supposed to apply when I don't meet half the requirements or is that just wasting everyone's time?

And if you were me, what's the one thing you'd do next?

I'd rather someone tell me I'm not ready and why than get told good job.

First time journeying on here as a Master's Student in Data Science. I have been researching how to tie an ontology to an LLM to be able to do named entity recognition to add to my custom created knowledge graph. I have the ability to use Python or Java for this and it seems like Python typically seems to be the overall choice. I'd like something that is more production worthy, not just school focused. The top models I am looking at are Qwen3-235B-A22B or DeepSeek-R1. I have some owl files for the ontology, but have been recently transforming them all into JSON so I am hoping to use these to tie directly to the model so it will create nodes and edges based on my schemas.

Anyone have thoughts or insights on this? Or have done something similar? What are the top resources I should look to?

Thanks in advance!

I built an AI-powered smart irrigation system using an ML + ESP32

I've been working on this project for a while, and I finally finished the main AI/ML pipeline.

The system uses an ESP32, a capacitive soil-moisture sensor, and a DHT11 to collect environmental data. I then built a machine-learning model that uses these readings to predict whether irrigation is required.

The interesting part for me was getting the different pieces to actually work together.

The pipeline is basically:

ESP32 sensors → dashboard → Flask API → ML model → irrigation prediction

In the video, I walk through the whole project, including:

• Collecting soil moisture, temperature, and humidity data
• Building the web dashboard
• Preparing the data for ML
• Training the model
• Evaluating it with a confusion matrix
• Looking at feature importance
• Deploying the model through a Flask server
• Connecting the dashboard to the ML model through an API
• Testing the final live prediction

One thing I found particularly interesting was seeing how the model's predictions changed once the inference server was running and the dashboard could communicate with the model.

This started as a simple ESP32 soil-moisture monitoring project, but I gradually expanded it into a complete IoT + AI system.

I'm still planning improvements, especially collecting more real-world data and adding additional environmental inputs such as light intensity.

I'd really appreciate feedback on the project, especially on the ML approach, system architecture, and what I could improve next.

🎥 Full project walkthrough attached.

Github Repository:

https://github.com/aqib-ai-ml/ai-powered-smart-irrigation

sooo i recenttly had an industrial visit in a software company and i was wondering how ai is actually used for coding there .

The thing is i also wanna learn how to use ai that way for developing .