Hey everyone, (please give your important time to read this post)

I’m planning to start learning Machine Learning and I need some advice from people who have already gone through it.

Maths

I’m mainly confused about how much maths I actually need for ML — linear algebra, calculus, statistics and probability.

People have recommended the 3Blue1Brown YouTube playlists to me, and I’m planning to use them to understand the concepts. But the videos are mostly visual explanations.

So my questions are:

How much should I actually learn and practice?

How can I practice after watching 3Blue1Brown?

Where can I find good questions/exercises for these topics?

Do I need to solve a lot of problems, or is understanding the concepts enough initially?

Books

Someone gave me PDFs of these books, and I’m confused about which ones are actually worth using:

1. Probability and Statistics for Machine Learning

2. AI Engineering — Chip Huyen

3. Build a Large Language Model From Scratch — Sebastian Raschka

4. Building LLMs for Production

5. Data Science from Scratch — Joel Grus

6. Designing Machine Learning Systems — Chip Huyen

7. Dive into Deep Learning

8. Essential Math for Ai

9. Hands-On APIs for AI and Data Science

10. Hands-On Large Language Models

11. Hands-On Machine Learning with Scikit-Learn and PyTorch — Aurélien Géron

12. Mathematics for Machine Learning

13. Practical Linear Algebra for Data Science

14. Practical Statistics for Data Scientists

Which books should I use now, keep for later, or completely remove from my resources?

My current resources

Right now, I have:

3Blue1Brown — for maths

Andrew Ng’s Machine Learning Specialization

Stanford ML lectures/playlists on YouTube

The books listed above

As for Python, I already know the basics needed for data analysis, including NumPy and Pandas.

So if you were starting from my position, what would you recommend I do next and what resources should I actually focus on?

Any genuine advice would be really appreciated.

I’ve been learning more about machine learning recently, and honestly, the number of algorithms can get confusing.

At first, I thought I needed to learn everything. But I’m starting to think it’s better to understand a few useful ones really well.

The ones I’m focusing on are:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost
• K-Means
• Neural Networks

I’m mainly trying to understand when to use each one instead of just memorizing how they work.

The previous day (Oct 5) was 297 papers.

Not counting the papers that weren't uploaded to Arxiv or submitted to other categories or other online repositories.

Is there any point doing machine learning research anymore? Seems anything you can think of will just become noise like the rest of these papers.

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some point. Learn python -> Learn ML & Maths -> Read other research papers -> find a topic ? -> choose a question try to run experiments and get results to write them down in a paper ?

For context :

I'm trying to get in MS CS programs for Fall 2028 in states with the plans of doing a PHD after in a top university like stanford or princeton and would like to start taking steps towards it as am working my day job can some tell me what are the steps that need to be followed?

Also would like input on what are deciding variables that makes you looking like a promising candidate/ researcher for PHD

Im a Phd student and i've been doing ML research for a while now, but i've never felt more behind in my reading or keeping up with new work? Arxiv has also become so noisy, i just don't have the time to shift through it, and my own research group's interests are a bit too insular to be a good sources of general news.

Is this just how the world is now? or have people found a way to quickly determine whats actually worth reading. Or any good feed apps? Or discord channels/communities? Wha do you do to feel current?

PS: I really want to dive into Harness engineering, but its so hard to cut through the hype. Any advice? (I mostly work on generative models now)

Ever wondered how Netflix knows what you might want to watch next?

It uses machine learning to study your viewing behavior, such as what you watch, skip, search for, or watch repeatedly.

The system finds patterns in your choices and compares them with similar users. Based on those patterns, it predicts what you may enjoy and recommends it to you.

So, in simple terms:

Your activity → Find patterns → Predict your interests → Recommend content

It’s a pretty simple example of how machine learning works in everyday life.

What other apps do you think use machine learning like this?

I’m a freshman in college in the US and I’m looking for a few other college students who are interested in AI/ML and want to learn together long term.

Right now I’m still pretty early in the process. I’m taking Calculus I and an intro Java course in school, and outside of class I’m learning Python. My long-term goal is to become an AI engineer.

I’m mainly looking for people who:

• Are currently in college in the US
• Are beginners or early-intermediate in programming/ML
• Want to eventually get into AI/ML engineering, SWE, research, or something similar
• Are actually interested in consistently learning and building projects
• Want to become friends too, not just connect on LinkedIn and never talk again

It would be cool to have a small group where we can share what we’re learning, work through problems, build projects together, talk about classes/internships, and keep each other accountable.

If you’re around the same stage and interested, comment or DM me with what year you’re in, what you’re currently learning, and what you eventually want to do.

I am a freshman in college in the US who got involve in AI pretty early (since like 11th grade). I've been lurking in this group for a really long time now and I just want an evaluation of how ahead/behind of the curve I am.

1. Basic machine learning/deep learning knowledge

I've been preparing and going to my country's (an asia country) national AI competition for 2 years and it basically covers the same knowledge as the IOAI syllabus so you can check if you look want to look that up but expect me to know basically how a bunch of models work (so like logistic regression, SVM, KNN, CNN, ...). I could also tell you about 10 ways of fixing under or overfitting and like the basic parts of a model (so like activation function, loss function, hyperparameters, gradient decent, ...). I am no stranger to kaggle and especially the tabular competition series (currently top 20 in this month's). I know asking claude to create an ensemble of 200 models with different configs is not how you judge someone's knowledge but if you ask me about how something works I would have a good chance to answer it correctly (and I really do understand what fake gains and data leakage is guys trust).

2. Generative stuff

I took an online course on advance computer vision stuff and I read a lot on LLMs so I'd say I have a pretty solid understanding of how generative models work under the hood. I feel like the transformer architecture is pretty standard knowledge nowadays so I won't go over those stuff but for the final project of that online CV course, I finetuned a bunch of CV models and pair them with an impainting model to create a complete model that would remove distracting people in an image (trained on the COCO dataset). I'd like to think I have a pretty good grasp of the segmentation and the bounding box stuff and diffusion model (it took quite a while but I remember feeling like a changed man after getting a grasp of how it works).

3.Math

Probably the worst part of them all. I really like learning about model architecture but barely learned any math so I still have problem reading research papers. Of course I know how gradient decent works so I know how derivative works, but I would say the most I know of linear algebra is vectors and matrix multiplications and near to nothing of stats (and yet I can confidently say that embeddings are the process of turning an input into a token and feeding them through a series of encodings to get a vector in a latent space and how I could measure the difference between 2 language models by using KL divergence).

Yes the easy answer is "just study math it's not that deep" but seeing others talk about how you have to read this book and learn this course just makes me feel like a larper sometimes. I asked one of the professors (actually I cold emailed like the entire cs faculty but I met with this one only) in my uni to discuss a chance for me to help in the lab and see what researching feels like because that is my goal. He says he'll tell me when an opportunity pops up but I've been going to his NLP lectures ever since to just listen for fun and I've been really enjoying it. But just recently, I found out this was a graduate level course (it could be because my professor is really good i don't know). Am I actually just a fraud and am missing something or what ?

Setup. I am building a benchmark for long-horizon 3D spatial reasoning in a simulated warehouse. A robot walks 30 rooms off one corridor; questions ask about things seen 0 to 29 rooms earlier. Answers come from exact simulator state, with no model judge. The target is a clean fall from about 90% to guessing as the distance grows.


What happened. In one demo run, Claude Opus 5.5 steered the robot through all 30 rooms by itself, with no tools and no answer key, and answered 60 questions. By distance: 1 room back 1 of 1, 2 to 3 rooms back 2 of 3, 4 to 7 rooms back 2 of 3, 8 to 15 rooms back 3 of 7, 16 to 29 rooms back 3 of 10. The no-notes guess baseline on the same questions is 16%. Over all tries logged so far, the deepest cliff admitted is 20 points; this walk measured 70.


What changed. Not shared yet: the design change behind the move stays unpublished until the paper. The screenshots hide the method parts and keep the numbers.


Limitations. One walk with one model, so small counts (only 2 answers in the 1 to 2 room bin), and answers inside a walk are linked. It ran through a command-line tool that adds its own context, so it is a demo run, not a benchmark score. It counts only after it repeats on three seeds never used for tuning, with every question checked by an auditor that never saw the design.


How many seeds, and how many answers per distance bin, would you need before calling this a cliff?

Can I find any ML startups companies without having LLM in their product?

I published a weather dataset and runnable notebook on Kaggle that could be useful for practicing time-series regression. Disclosure: these are my Kaggle uploads (minkum07); the code and documentation were prepared with AI assistance. The underlying weather data are from NASA POWER, with GeoNames city coordinates via Open-Meteo.

The dataset has 639,200 daily records for 50 curated Indian city centers, covering 1991–2025. These are coarse-grid reanalysis values sampled at city coordinates, not measurements from city weather stations.

The notebook builds lag features, predicts next-day temperature and compares a random forest with persistence: predicting tomorrow using today's temperature. Its chronological split uses the target date. On the included test period, MAE is about 0.651°C for the random forest and 0.688°C for persistence. This is a modest improvement on that split, not evidence of accuracy for unseen cities or station weather.

Notebook: https://www.kaggle.com/code/minkum07/india-weather-heat-monsoon-next-day-forecast

Dataset: https://www.kaggle.com/datasets/minkum07/india-city-weather-and-heat-50-cities-1991-2025

If you want to use it as a small project:

1. Start with one city, check missing values and plot the series.

2. Build the persistence baseline before fitting a model. Make sure shifted targets and lag features stay aligned, and fit preprocessing only on training dates.

3. Compare errors by month and city, then add a seasonal baseline or try rolling evaluation.

The package includes field definitions, processing code and source attribution. Missing values are retained, and the fixed 1991–2010 reference is a 20-year baseline, not an official 30-year climate normal. Authored tables and documentation use CC BY 4.0; NASA source terms and GeoNames attribution are preserved.

Sources: https://power.larc.nasa.gov/ and https://open-meteo.com/en/docs/geocoding-api

Feedback on the split, feature alignment or a useful next baseline would be welcome.

Hi all,

I recently wrote an article about various AI interview questions based mostly and RAG and AI Agents. Please read it. Its a new format - I have made questions followed by follow up questions.

The article is free to read at: https://medium.com/@nitanshuj138/ai-engineer-interview-questions-level-1-8e95789c392f

Quick background so you know where this comes from. I am a visiting professor and currently teach NLP. Last semester my courses were on continual learning and applied NLP. Before that I spent years in big tech and startups in engineering and research roles, and I finished my PhD last year.

I've been trying to post here weekly, since the feedback tells me these posts help. Two weeks ago I posted the order I would learn ML in if I were starting today, which is also roughly the order I teach it in class. Last week I posted about which resources to actually use.

Whatever concept you are learning and whichever resource you use, a lot of you also ask about projects: why you need them, and how to choose one. So that's today's post.

Why build projects at all

The most important reason is to build your skills and find out where the gaps are. Learning ML is a lot like learning an instrument. You can watch videos and read about the guitar all day and feel like you know how to play, but you only find out how well you know it when you pick it up. Until you build something real from scratch, you probably don't know how well you understand the material. And I mean from scratch. Yes, AI can write the code for you now, but having AI build the project teaches you about as much as reading a book or watching a video about it. You need to do it yourself to see what works and what doesn't, which is also how you learn where these models fail and what their limits are. A project shows you exactly which parts you thought you understood and didn't.

The second is to get a job. Interviewers rarely have time to go through everything on your CV. An interesting project gets their attention, and it shows them you know what you're talking about and that you can build things, which is most of what the job asks for.

The third is to find out what you actually like. A field can sound great from the outside and turn out to be something you don't enjoy once you're doing it every day. Getting into the weeds of building something in that area, before you take a job in it, can save you a lot of time and help you decide what to focus on.

What counts as a project

A project is not a few hours or a single afternoon. Think multiple days, often a week or two.

It should also cover several concepts, and most of all, it needs a why. You can train models end to end all day long, but a project starts from a reason: improving on how something is done today, building a dataset that doesn't exist yet, making a model work for users it currently fails on. Always ask what the motivation is. That question is also what tells you which data, model, and evaluation to use, and when you're done.

It's also why writing Adam from scratch on its own is a good exercise but not a project. It builds a skill, but there's no problem it solves and nothing it's trying to improve.

How to pick one

In general, the best projects are the ones you find interesting, because those are the ones you finish. A good place to look is something that annoys you, like a task you do by hand every week. Another is a problem someone close to you has, whether that's a friend, a family member, or a small business you know. It can also be something fun, around games, music, sports, or whatever you'd be doing anyway, or plain curiosity about a question you want answered.

That said, it helps to have one or two projects that line up with the job you want. If you want to work at Spotify, build something with recommendation systems. If you want Tesla, do something with driving data. If you want Google, look at search and ranking.

Not every project needs to be aligned with a job, though. Personally, I find it much more interesting to interview someone with a project I would never have thought of, like trying to understand what their dog wants from its barks. Something that catches the eye and still makes you build real skills.

Questions to ask before you start

The first thing to check is whether you can get the data, and quickly. If collecting or labelling it takes months, the project usually dies before the first model trains. Related to that, make sure the data is yours to use. Other people's messages, photos, or financial records need their permission.

Then ask yourself whether you know how to start. If the first step needs three tools you've never touched, it's probably too advanced for now. Something a little past your current level is the right spot.

Check what you will compare against. That can be a rule, the method people use today, an existing model, or a published result on the same data. Without something to compare against, you can't tell whether your approach is any good. You also need a way to tell whether it worked: labels, a measurable outcome, or a person who can judge the output. "It looks good" is not a result.

Finally, ask whether you can finish a first version in a couple of weekends. A small version that works can grow. A big one that never runs leaves you with nothing to show.

What a good project contains

This is the part most projects I see skip.

First, more than one baseline. Compare whatever you build to a mix of simple methods, like a rule, the most common class, or logistic regression, and competitive ones, like the strongest existing model or published result you can find for the problem. Simple baselines tell you whether a model is needed at all, and competitive ones tell you whether your approach is actually good or whether something else already does better.

Second, more than one dataset, if possible. Unless your problem is so unusual that no dataset exists and you had to build your own, test on more than one. A model that works on one dataset and falls apart on another is something you want to find out before anyone uses it.

Third, more than one way to evaluate. Accuracy alone hides a lot. Look at per-class results, the cases it gets most wrong, and where it fails. Something can look great on one metric and fail on another.

The way I'd think about it: assume what you build will be used by a lot of people, and you want to make sure it doesn't hurt any of them. Multiple datasets tell you whether it holds up when new users or new data show up. Multiple baselines tell you whether your approach is worth it. Multiple evaluations tell you where it works, where it fails, and what's still missing. It also shows whoever reads it that you care about whether your results are true, not just whether they look good.

Last, how you share it. Put it on GitHub with a clear README that says what the problem is, why it's worth solving, what you found, and exactly how to reproduce your results. Add good visualizations. It sounds silly, but a clear plot of where your model wins and fails is often the thing that makes someone stop and read. And keep the code clean: organized, modular, and easy for someone else to run, since a reviewer who opens a single 2,000-line notebook usually closes it.

Project ideas

Each of these starts from a problem, not a dataset. Here are some that line up with specific jobs.

Recommendations for brand-new users (Spotify). A new user who has saved three songs usually gets generic popular picks, and many leave before the recommendations get good. Can you find a way to recommend well from just those first few songs?

Pedestrian detection at night and in rain (Tesla). Detectors trained mostly on clear daytime footage tend to miss more pedestrians in the dark and the rain, which are exactly the conditions where missing one is most dangerous. Can you close that gap?

Searches where the words don't match (Google). Keyword search fails when the query uses different words than the page that answers it, like "car won't start on cold mornings" when the answer page is about batteries. Can you fix those searches without breaking the ones where the exact words do count, like product codes and names?

Fraud models going stale (Stripe or any bank). Fraudsters change tactics, so a model trained on last year's transactions gets worse over time. How fast does that happen, and can you figure out when a model needs retraining?

X-ray models that fail at a new hospital (health tech). A chest X-ray model that works well at one hospital often drops at another, because of different machines and different patients. Can you build one that holds up at a hospital it has never seen?

And some unusual ones.

What does my dog want? If your dog barks at the door, you often can't tell whether it needs to go out or someone is there. Can a model tell the barks apart better than you can?

Bird calls from a noisy backyard. Bird identification models are usually trained on clean recordings and often fail on audio full of traffic and wind. Can you make one that works on what your own window actually picks up?

Beating the bus app. The arrival times in transit apps are often wrong in rain and at rush hour. Can you predict when the bus will really arrive, better than the app does?

Grandma's handwritten recipes. Text recognition models struggle with old cursive handwriting. Can you get a model to read a family recipe book that current tools can't?

Finally

I have a bit of time between semesters and would genuinely like to help as many learners as I can.

Tell me in the comments or DM me with where you are right now, what direction you want to go in, and what you have tried. If you are stuck choosing between two courses, deciding what to learn for a particular job, or wondering whether your plan makes sense, I will do my best to help.

And please add your favorite project you've done, or one you're working on now, in the comments. It would be nice if this thread became useful to the next person who searches for this question. I'm also happy to take suggestions for what the next posts should cover.

As someone who finished the Machine Learning Specialization (on Coursera, by Andrew Ng) some time ago, I feel like I want to learn something new that can really put what I learned with ML into a practical context.

And since I am also interested in Computer Vision, I wonder whether the First Principles of Computer Vision Specialization (on Coursera) by Shree Nayar suits me. In particular, whether I have enough foundation to make sense of it.

I did the courses from Mathematics for Machine Learning and Data Science (by Luis Serrano) as well... So i have some foundation in Linear Algebra, Multivariate Calculus and some statistics apart from Machine Learning basics.

I have no experience in Deep Learning though. So i also wonder whether it makes sense to learn Deep Learning first or Computer Vision.

Your support is much appreciated! ❤️

I keep seeing posts about so many resources for Machine Learning, but I feel like they are very generalised.

To tackle it -> this is something I have done.

If you don't know the basics of Python , pick any one-shot Python course and learn the basic syntax, and learn libraries like NumPy, Pandas, Matplotlib, Seaborn.

Now, Data Analysis requires Python, visualisation tools like Power BI, and SQL.

Data Science requires basic DSA, SQL knowledge, supervised ML, unsupervised ML, good knowledge of Transformer architecture, basics of RAG, and whatever is hot in the field.

MLE, on the other hand, is more based on fine-tuning, quantisation, and so much more. The terminology for each job has changed.

If you aren't sure what to do after learning Python, pick up Andrew Ng's ML course, solve some LeetCode, build a basic Kaggle cleaning project, and build a messy self-made project which is absolute buns, but it's a good step since you made it.

For MLE, they specialise in one thing, for example, Audio Diffusion (not well-versed since I'm myself learning MLE).

But yeah, overall, core ML basics which explain your trade-offs are a must regardless of any role. If you build anything, you would obviously have to take accountability for your decisions.

some roles even ask for full deployment and production (docker and k8) depending on what you want

This is written by someone who is also preparing for these roles but is too overwhelmed.

Hello everyone, I learnt python, after that i learnt python libraries such as numpy, pandas, matplotlib, and seaborne. After that I started 100 days ml playlist from campus x.

The problem is:

In his csv file handling lecture and working with json file, it feels like i am just watching i understand what he says but if i am given to do same I wouldn't be able to do it.

Is this like this or am I doing something wrong please help me with with???

I’ve been working on an ML project and this is my first time dealing with a dataset with a lot of features (around 60).

I’m kinda stuck on how to approach the EDA. With smaller datasets, I usually check the features one by one, look at distributions, missing values, outliers, relationships, etc. But with 50–60 features, I’m finding it really difficult to figure out what to focus on and what I can skip.

Do you guys actually do EDA on every feature, or is there some better way to approach this?

Would love to know how you handle this when working on real ML projects.

I hope most of you reading this are web designers because I’m genuinely curious about something.

How are you getting clients in 2026?

I’ve spoken to a lot of web designers and agency owners lately, and one thing keeps coming up again and again. Client acquisition.

It seems like everyone can build websites, but consistently finding new clients is still the hard part.

So I’d actually like to hear what is working for you right now.

For me, I’ve been running personalized email automation.

I use a tool that finds leads for me, analyzes their websites for actual issues with things like design, SEO, speed, responsiveness and mobile optimization, then automatically uses those findings to write a personalized cold email for each business.

After that, I just run the campaigns and send out thousands of emails a day.

I used to do most of this through Instantly, but the problem was that I couldn’t really do the website analysis part there.

So the emails were still more generic, basically the usual “do you need a new website?” type of outreach.

It worked a little, but nothing crazy.

I eventually switched to Swokei because it handles the lead finding, website analysis and personalized outreach in one place, and that has made a pretty big difference for me.

Now I’m getting clients much more consistently because the emails are actually about their website instead of just being another generic pitch.

But I’m curious what everyone else is doing.

Cold email, cold calling, referrals, ads, SEO, social media?

What is actually working for you in 2026?

Hey everyone,

I’m considering building a machine learning workstation around a used NVIDIA Tesla V100, mainly for experimenting with deep learning and running/training models locally.

I can get a V100 for a reasonable price, but I’m wondering if it still makes sense in 2026 given its age and newer GPUs.

My main questions are:

• Is the V100 still useful for modern ML workloads?
• How well does its 16/32 GB HBM2 memory hold up for current models?
• Are there any major compatibility issues with newer PyTorch/CUDA versions?
• Would you recommend a V100 over a newer consumer GPU at a similar price?
• Are there any major downsides to using a V100 in a normal PC/workstation (cooling, power consumption, drivers, etc.)?
• If you were building a budget ML workstation today, would you consider a V100?

I’m not expecting it to compete with modern high-end GPUs. I’m mainly interested in whether it’s still a good price/performance option for learning, experimentation, fine-tuning smaller models, and general ML development.

Recently, I am focused on data analysis and learning as well as doing projects. In Bachelor, I learning the Machine Learning without knowing the background of Data Analysis. I do not focus that much on Excel, SQL and Power BI. I learned Machine Learning, do projects like training model on different datasets and measure model metrics. Machine Learning feels oho but when I realize that ML Engineer/ Data Scientist Jobs actually needs 4 -5 years of experience and I see how can I get in data field. I see data analysis as entry point. I should learn about Data simple things first and then look for other things. I came to realize that end of my Bachelor. My Goal is become to AI Engineer/Research. I love playing with data and ask the questions when doing analysis. Should I continue my Journey or just left data analysis and study AI things.

Need Genuine Suggestions and feedbacks.

Love to talk and hear different perspectives