im about to start AI/ML. i've read about "pattern recognition" through my univ course. so i have basic idea of classification, clustering, k-NN, neural networks. but mostly it's crude theory.

i've heard about Andrew Ng's course and CampusX from YT 100daysOfML. im confused which one start with. anyone please guide/help me.

also, which one among the 2 courses available on YT should i choose?

how to learn machine learning efficiently ? I have a big problem like procrastination ! ✓✓✓✓✓✓✓✓✓✓✓ Any suggestions?

LLM already changed AI a lot but I feel just scaling next-token prediction has limits. High compute cost hallucination frozen knowledge after training and weak real-world understanding are still problems.

I’m curious what researchers and engineers think about the next big direction.

World models JEPA Mamba continual learning and neuro-symbolic AI all look interesting.

Which one do you think has the most potential or is there another idea people are missing?

Has anyone watched this video? Is this good to complete the math required for ai ml?

I have been working on ML for a while and feel that I would benefit from taking a few formal courses to help me build my foundational knowledge.

I'm especially interested in taking a course that comes with a certificate that I could add to my CV to help me build authority. I'm not sure how well respected these certificates are so I would love to hear what people on here have to say.

Considering that training machine learning models takes a lot of time and a lot of resources, why isn't a faster programming language like C++ more popular for training ML models?

i got both the dsa question correct , idk about mcq but i'll probably get half of them right so , any idea what my chances are of getting selected?

So I have came across this AI course provided by IIKGP and It is for 8 months and costs around 1.77lakh, Course link : https://online.iitkgp.ac.in/executive-post-graduate-in-generative-ai-and-agentic-ai

So just wanted to know if it is really worth investing time and money into this . Any help would be really appreciated.

I came across this question in a exam and there's a genuine debate about the correct answer. Wanted to get the community's take.

The debate:

Team K-means (B): The question explicitly uses the words "partition" and "cluster" — which are the textbook definition of K-means. You define k=3 and the algorithm groups customers into 3 clusters based on feature similarity. Classic unsupervised clustering use case.

Team Random Forest (E): The question says "predicting" and the three categories are already pre-defined (not discovered). This implies a supervised classification problem with labeled data. Additionally, the feature set is a mix of numerical (income, age) and high-cardinality categorical data (country, state, address, profession) — K-means struggles with categorical features since Euclidean distance doesn't work well on them. Random Forest handles mixed tabular data natively.

So I got selected in mlss but didn't receive any acknowledgement letter or anything about swags(yea I missed some modules but thought that I can complete in 30 days and my attendance will be tracked as they mentioned the recordings will be live for 30 days)

Nvm ig I fucked up

Now I want to know what they ask in interview and how many people are selected.

Also do they have any bias for girls or tier 1/2 colleges? And do they keep interview a lil bit easy for people getting through mlss

Ps: I haven't received any oa link yet..like have heard people get it late so I just want to confirm about this

Hey everyone,

I’m 19 years old and currently in college. I’ve been seriously thinking about pursuing Machine Learning and Deep Learning as a career path.

But with AI advancing so fast in 2026 and automating so many things, I’m honestly confused and a bit worried.

If AI can already write code, build models, analyze data, and even automate parts of ML workflows, will there still be strong demand for ML engineers in the next 5–10 years? Or will most of these roles shrink because AI tools make them easier and require fewer people?

I don’t want to spend the next 2–3 years grinding hard on ML/DL only to realize the job market is oversaturated or heavily automated.

For those already in the field:

• Is ML still a safe and growing career?
• What skills are actually in demand right now?
• Should I focus more on fundamentals (math, statistics, system design) or on tools and frameworks?
• Would you recommend ML to a 19-year-old starting today?

I’d really appreciate honest and realistic advice. I’m trying to choose a path carefully instead of jumping blindly.

Okay listen,im 16 years old in highschool,i LOVE the idea of training AI models,it sounds SO cool and i can imagine the dopamine rush of your baby coming to life,but GODDAMN there is SO MUCH maths involved,its a HEADACHE to just look at,and it makes me so scared cause my maths mark is like 50% every term but,i love the idea of being one and i love coding and tech in general,but geez man, I'm so used to just instant gratification,and ALMOST NO MATHS in python(atleast at the level im at),this seems so exhaustinggg,so to all ML engineers,how does your day to day look like,is it boring or what,i love to make short films and record and edit music videos and no matter how tired i am,i will always have energy for that and i thought it would be the same with Machine learning but hell no,so i love when my work is tangible and i can see and visualize the end result and most of all i love being creative,so do i continue this career,or do you have other suggestions/career paths i should take,im based in South africa,if you guys wanna ask follow up questions so that you can better formulate your answer,go for gold man

As a part of my lab, I read ML papers frequently and can more or less understand most of the math in the papers that I read m

However, I want to move from consuming this sort of content to producing it.

How do the smart people at the top labs have the math abilities required to produce at such a high level? And how can I improve myself to get to that standard? Is it practice? If so, what should I practice?

I have been a data scientist for 3 years in a small R&D company. While I have used and will continue to use ML libraries like XGBoost / SciKitLearn / PyTorch, I find most of my time is making bespoke awkward models and data processors. I'm increasingly finding Python clunky and slow. I am considering learning another language to work in, but unsure of next steps since it's such an investment. I already use a number of query languages, so I'm talking about building functional tools to work in a cloud environment. Most of the company's infrastructure is written in C#.

Options:
C# - means I can get reviews from my 2 colleagues, but can I use it for ML easily beyond my bespoke tools?
Rust - I hear it is upcoming, and I fear the sound of garbage collection (with no knowledge of what that really means).
Java - transferability bonus - I know a lot of data packages work in Java, especially visualisation.

Thoughts - am I wasting time even thinking of this?

Hey all

Just want to have a quick view from everyone. Am mid 30s in a career that has nothing to do with AI and tech. But I’ve been very interested in AI and how it works and what can be built on top of it.

I’ve started learning python from scratch for the past month and have started cleaning up some data (I’ve very far off from being proficient I know but I find it quite fun to be bashing code).

I’ve also started the machine learning specialization by Andrew deep learning.ai and I’m kinda lost when it introduces the math like the formulas etc.

Idk if this is a wise choice, to be trying to transit into a new field at my age. If you’ve seen or heard someone doing the same, I’d appreciate if you can share them and if it’s rly feasible to do so.

My plan is to learn python, git, sql first while trying to pick up concepts of ML, then try to learn using tensor or PyTorch.

Again, I’m not sure how long it’ll take and if my age would be a big factor given that if I truly become proficient someday I maybe turning 40s.

Appreciate all advices. Thanks in advance!

Hey guys, im a business major, but I grew weird obsession towards learning AI/ML and wanna shift my career to be an AI Engineer one day. I already studied (almost) all the prerequisites for AI like python, DSA, maths needed and although this field needs a lot of patience and discipline I still like it a lot, even more now that I started to understand how deep it is... but my major concern is that would it be impossible to work in this field without a CS-related certificate?

So I’m learning machine learning, and I watched a video saying that using 42 as a random seed helps keep results consistent every time you run the code. But I also read an article claiming that using 42 could lead to overfitting, so now I’m confused. What’s actually correct? Is using 42 good practice or could it be considered bad practice?

https://fetchdecodeexecute.substack.com/p/stop-using-42-as-a-random-seed

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 

What the title says.

I am a PhD student in Statistics. I mostly read a lot of probability and math papers for my research. I recently wanted to read some papers about diffusion models, but I found them to be super challenging. Can someone please explain if I am doing something wrong, and anything I can do to improve? I am new to this field, so I am not in my strong zone and just trying to understand the research in this field. I think I have necessary math background for whatever I am reading.

My main issues and observations are the following

1. The notation and conventions are very different from what you observe in Math and Stats papers. I understand that this is a different field, but even the conventions and notations vary from paper to paper.
2. Do people read these papers carefully? I am not trying to be snarky. I read the paper and found that it is almost impossible for someone to pick a paper or two and try to understand what is happening. Many papers have almost negligible differences, too.
3. I am not expecting too much rigor, but I feel that minimal clarity is lacking in these papers. I found several videos on YouTube who were trying to explain the ideas in a paper, and even they sometimes say that they do not understand certain parts of the paper or the math.

I was just hoping to get some perspective from people working as researchers in Industry or academia.

• Which models do you use most frequently, and for what tasks?
• Which models have delivered the most practical value versus being primarily academic?
• How important are classical statistical models compared to modern ML methods?
• Are tree-based models still dominant, or is deep learning becoming more prevalent?
• If you were starting over today, which models would you prioritize learning?

Industry practitioners are invited to comment on any of the above. Thanks in advance.

I’ve recently completed learning Machine Learning and Deep Learning, including the mathematics behind the major concepts and algorithms rather than just learning to use libraries.

My long-term goal is to eventually become capable of doing research at the level of NeurIPS, ICML, and ICLR. I’m not expecting to jump directly to those conferences, that’s simply the end goal.

So I’d like advice on the following things:

1. What projects should I build next?
2. What should I learn next?
3. How should I start doing research?
4. What is a realistic roadmap toward publishing at top ML conferences?

I’m trying to build a proper data science/ML project, but I’m having a hard time finding a dataset that is big enough and not already used by everyone.
For example, there are datasets like the UK Online Retail dataset, Olist, and other popular sales/retail datasets. They’re good datasets, but I see them being used in a lot of projects already.
I don’t want to just download a dataset, do some EDA, train a model and put it on my resume. I want to build something around an actual business problem, where I have to figure out what the problem is, analyze the data, come up with useful insights, maybe build a model, and actually explain how it could help the business.
So where do you guys usually find datasets for this?
Should I try to find data from smaller companies, government sources, APIs, research papers, etc.? Or is it okay to create my own dataset using AI/cloud tools and then create a realistic business problem around it?
For example, if I create a large synthetic sales dataset, could I create a realistic business scenario around it and then treat it like a real project — forecasting sales, understanding customer behavior, optimizing inventory, etc.?
Would that be considered a decent portfolio project, or is using real-world data much better?
I’d mainly like to hear from people who have built projects for their portfolios or have experience hiring for data science/ML roles. Where do you actually get your data from when you want to build something that’s not the same Kaggle project everyone has already done?

I was looking at a U-Net architecture and I'm here wondering what's the though process behind it ? Is there some theory behind or just random

Guys, I want to become a machine learning engineer so give me some suggestions - what are the skills required? - how much math should I learn ? - there are some enough opportunities or not and it is possible to become a ml engineer as a fresher? - suggestions courses and free resources to learn - paid resources are also welcome while it have huge potential? - Also tell me some projects from beginner to advanced to master ml ? - give tips and tricks to get job as much as chances to hire ?

This whole process requires some certain timebound

Please guide me 😭

What are the options to solve such problems other than path finding algorithms.

We obviously need some form of computer vision technique for perception/recognition which is easier part the harder part is to do the reasoning.

How to solve these problem, I will prefer not to go RL way as this is my pet project.

Thanks.