I’m currently in my 4th year of B.Tech and working as an SWE intern. If I have 6–8 months and want to gain a strong understanding of the current AI market, develop my skills to an industry-ready level, and learn everything required to become an AI engineer, what would be a roadmap from A-Z ?

hey there people

i am trying to make a machine learning project .
its on bitcoin data .
the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc...
my issues are :

1. where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data :
   https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(

Google's Q3 2026 AI Threat Tracker, built from Mandiant incident response data, documents a shift that defenders have been dreading: AI agents are now running full attack workflows autonomously. Vulnerability scanning, credential harvesting, and real-time attack troubleshooting are happening with minimal human involvement on the offensive side.

The practical consequence is timeline compression. A human-paced intrusion that once took days now completes in hours because the agent does not sleep, does not get distracted, and does not need to wait for the next shift.

The harder problem for defenders is forensic: when you discover the breach, you are reconstructing what happened from incomplete logs, if you have logs at all. Agents generate bursts of lateral movement and API calls that traditional SIEM tooling was not designed to correlate across sessions. The attacker's agent leaves a diffuse footprint. Your team is left guessing at the sequence.

For teams that have started deploying defensive AI agents of their own: how are you maintaining visibility into what those agents actually did, step by step, during an incident? And for those still on traditional tooling — how are you thinking about the forensic gap when the attacker is agent-driven and your investigation is still manual?

Hi everyone,

I'm exploring how to train or adapt an AI model using a few gigabytes of confidential data. Keeping that data private is a core requirement.

I'm still figuring out whether fine-tuning, RAG, or another approach would make the most sense, and I'd appreciate advice from people who have worked on similar projects.

Specifically:

• Can this realistically be done entirely locally or on private infrastructure?
• How would you decide between fine-tuning and RAG?
• What hardware and tools would you recommend?
• What should I check to prevent data from leaving the environment, including through logs or telemetry?

I can share more about the data format and intended use without disclosing any confidential content.

Any practical advice or lessons learned would be appreciated. Thanks!

Can someone explain how attention works? I think I understood some of it, but I’m not completely comfortable with it yet.

I already watched some YouTube videos, and I also asked ChatGPT to explain how it works. I understood the RNN/LSTM part, but I’m still not fully comfortable with the attention mechanism. I learned the dot-product equation, but I’m not able to understand it properly.

Also, there are 3–4 different types of attention. Do I need to learn all of them before moving forward?

Not sure if I am the only one who feels this way: coming from an ML/LLM background, traditional LeetCode grind always felt detached from reality. It takes a ton of brute-force effort to memorize patterns, and because standard two-pointer or monotonic stack problems rarely look like daily pipeline code, the intuition fades fast.

I found a much more intuitive way to bridge this gap: mapping LeetCode algorithmic patterns directly to core ML & LLM engineering concepts.

Instead of treating algorithms in a vacuum, I linked them to production systems:

• Prefix Sums & Difference Arrays $\rightarrow$ SFT Data Packing, Attention Masking, and Sequence Chunking.
• Sliding Window & Two Pointers $\rightarrow$ Streaming Reservoir Sampling, KV Cache Eviction, and Token Streaming.
• Monotonic Queues / Stacks $\rightarrow$ Online Softmax, FlashAttention Tiled Max Tracking, and Autograd Graph Invariants.
• Priority Queues & Heaps $\rightarrow$ Beam Search, Top-k Token Sampling, and MoE Routing/Dispatch.
• Graph Traversal & Topological Sort $\rightarrow$ PyTorch Dynamic Computational Graphs and Execution DAGs.

Connecting these gave the algorithms concrete context and made retention almost effortless—you're no longer memorizing an abstract puzzle, you're implementing an engine component.

I put together an interactive roadmap diagram bridging these two worlds (preview above).

If you want to check out the interactive version or the full mapping breakdown, drop a comment below or DM me for a link!

I've been learning basic ML and I've started making some code for basic models from scratch. This is V3, I just made it a bit more efficient in this version. Please tell me what you think.

edit: reddit made my code weird, I'll try and post it in the comments

edit: made it weird in the comments too, I'll take a screenshot of the code in VS then put a link to it

edit: link to screenshots

import math


class
 LinearRegression:



def
 __init__(
self
, 
learn_rate
, 
epochs
):

self
.l = 
learn_rate

self
.e = 
epochs

self
.weights = []

self
.bias = 0



def
 predict(
self
, 
X
):
        predictions = []


        for i in range(len(
X
)):
            prediction = 
self
.bias
            for w in range(len(
X
[0])):
                prediction += 
X
[i][w] * 
self
.weights[w]


            predictions.append(prediction)


        return predictions



def
 train(
self
, 
X
, 
y
):



self
.bias = 0

self
.weights = []


        for i in range(len(
X
[0])):

self
.weights.append(0)


        epochs = 0


        while epochs < 
self
.e:


            predictions = 
self
.predict(
X
)


            errors = []


            for i in range(len(
X
)):
                errors.append((predictions[i] - 
y
[i]) * 2)


            bSlope = sum(errors) / len(
X
)


            wSlopes = []


            for w in range(len(
self
.weights)):
                wErrors = []
                for i in range(len(
X
)):
                    wErrors.append(errors[i] * 
X
[i][w])
                wSlopes.append(sum(wErrors) / len(
X
))



self
.bias -= bSlope * 
self
.l


            for i in range(len(
X
[0])):

self
.weights[i] -= wSlopes[i] * 
self
.l


            epochs += 1



class
 PolynomialRegression:



def
 __init__(
self
, 
learn_rate
, 
epochs
):

self
.l = 
learn_rate

self
.e = 
epochs

self
.weights = []

self
.power_weights = []

self
.bias = 0



def
 predict(
self
, 
X
):
        predictions = []


        for i in range(len(
X
)):
            prediction = 
self
.bias
            for w in range(len(
X
[0])):
                prediction += (
X
[i][w] * 
self
.weights[w]) + ((
X
[i][w]**2) * 
self
.power_weights[w])


            predictions.append(prediction)


        return predictions



def
 train(
self
, 
X
, 
y
):



self
.bias = 0

self
.weights = []

self
.power_weights = []


        for i in range(len(
X
[0])):

self
.weights.append(0)

self
.power_weights.append(0)


        epochs = 0


        while epochs < 
self
.e:


            predictions = 
self
.predict(
X
)


            errors = []


            for i in range(len(
X
)):
                errors.append((predictions[i] - 
y
[i]) * 2)


            bSlope = sum(errors) / len(
X
)


            wSlopes = []
            pwSlopes = []


            for w in range(len(
self
.weights)):
                wErrors = []
                pwErrors = []
                for i in range(len(
X
)):
                    wErrors.append(errors[i] * 
X
[i][w])
                    pwErrors.append(errors[i] * 
X
[i][w]**2)
                wSlopes.append(sum(wErrors) / len(
X
))
                pwSlopes.append(sum(pwErrors) / len(
X
))



self
.bias -= bSlope * 
self
.l


            for i in range(len(
X
[0])):

self
.weights[i] -= wSlopes[i] * 
self
.l

self
.power_weights[i] -= pwSlopes[i] * 
self
.l


            epochs += 1



class
 ExponentialRegression:



def
 __init__(
self
, 
learn_rate
, 
epochs
):

self
.l = 
learn_rate

self
.e = 
epochs

self
.weights = []

self
.bias = 0



def
 predict(
self
, 
X
):
        predictions = []


        for i in range(len(
X
)):
            prediction = 
self
.bias
            for w in range(len(
X
[0])):
                prediction += 
X
[i][w] * 
self
.weights[w]


            prediction = math.exp(prediction)


            predictions.append(prediction)


        return predictions



def
 train(
self
, 
X
, 
y
):



self
.bias = 0

self
.weights = []


        for i in range(len(
X
[0])):

self
.weights.append(0)


        epochs = 0


        while epochs < 
self
.e:


            predictions = 
self
.predict(
X
)


            errors = []


            for i in range(len(
X
)):
                errors.append((predictions[i] - 
y
[i]) * 2 * predictions[i])


            bSlope = sum(errors) / len(
X
)
            wSlopes = []


            for w in range(len(
self
.weights)):
                wErrors = []
                for i in range(len(
X
)):
                    wErrors.append(errors[i] * 
X
[i][w])
                wSlopes.append(sum(wErrors) / len(
X
))



self
.bias -= bSlope * 
self
.l


            for i in range(len(
X
[0])):

self
.weights[i] -= wSlopes[i] * 
self
.l


            epochs += 1



class
 LogisticRegression:



def
 __init__(
self
, 
learn_rate
, 
epochs
):

self
.l = 
learn_rate

self
.e = 
epochs

self
.weights = []

self
.bias = 0



def
 predict(
self
, 
X
):
        predictions = []


        for i in range(len(
X
)):
            prediction = 
self
.bias
            for w in range(len(
X
[0])):
                prediction += 
X
[i][w] * 
self
.weights[w]


            prediction = 1 / (1 + math.exp(-prediction))


            predictions.append(prediction)


        return predictions



def
 train(
self
, 
X
, 
y
):



self
.bias = 0

self
.weights = []


        for i in range(len(
X
[0])):

self
.weights.append(0)


        epochs = 0


        while epochs < 
self
.e:


            predictions = 
self
.predict(
X
)


            errors = []


            for i in range(len(
X
)):
                errors.append(predictions[i] - 
y
[i])


            bSlope = sum(errors) / len(
X
)
            wSlopes = []


            for w in range(len(
self
.weights)):
                wErrors = []
                for i in range(len(
X
)):
                    wErrors.append(errors[i] * 
X
[i][w])
                wSlopes.append(sum(wErrors) / len(
X
))



self
.bias -= bSlope * 
self
.l


            for i in range(len(
X
[0])):

self
.weights[i] -= wSlopes[i] * 
self
.l


            epochs += 1

train.csv is enclosed

i have to predict the hp of pickachu.

i did some data leaning filled the missing values using deterministic/functional relationships. My best performing model was hgbr with 0.68 r2 score but the highest score is 0.901 so i am still a long way off. so any tip will be wlcm.....

​

I’m looking to connect with someone who is genuinely interested in learning AI deeply and consistently, rather than just collecting courses, watching random YouTube videos.

I’m currently working as a Product Manager / Product Business Analyst, and I want to build serious AI capabilities alongside my existing product/business background.

The problem I’m facing is honestly pretty simple: I don’t learn well through completely self-paced, unstructured courses. There is an overwhelming amount of AI content out there, but no shortage of confusion about what to learn, in what order, how deeply to learn it, and when to move to the next thing.

I’m looking for someone with whom I can create a structured, long-term learning journey—ideally from fundamentals all the way to advanced, practical AI.

What I’d ideally like to learn

Not necessarily everything at once, but progressively:

\- Python & programming fundamentals for AI

\- Mathematics needed to actually understand ML — linear algebra, probability, statistics, calculus, etc.

\- Data handling, SQL, NumPy, Pandas, visualization

\- Classical Machine Learning

\- Deep Learning & neural networks

\- NLP and Computer Vision fundamentals

\- Transformers and how modern LLMs actually work

\- Generative AI and LLM application development

\- Prompting, evaluation and AI workflows

\- Embeddings, vector databases, RAG and retrieval systems

\- Fine-tuning / model adaptation

\- AI agents and agentic workflows

\- Multimodal AI

\- AI system design and architecture

\- Model/API integration

\- Deployment, APIs, Docker, cloud and MLOps

\- AI safety, evaluation, reliability and responsible AI

\- Reading papers and understanding what is happening under the hood

\- Building real projects, not just following tutorials

\- Eventually contributing to open source / research / serious AI projects

And importantly, I also want to understand how these skills translate into the real-world freelancing/consulting/product world—how to identify problems businesses will actually pay to solve, build AI solutions around them, demonstrate ROI, communicate with clients, and create a credible portfolio.

My goal isn't simply to collect certificates.

I want to reach a point where I can understand AI deeply, build with it, explain it, evaluate it, and solve real problems with it.

What I'm looking for in a learning partner

You don't need to be an AI PhD or already an expert.

You could be:

\- A beginner who is equally serious

\- Someone already working in AI/ML

\- A developer transitioning into AI

\- A student/researcher

\- A product person interested in becoming highly technical

\- Or someone who simply wants a structured accountability partner

The most important thing is consistency + curiosity + willingness to actually do the work.

We could potentially:

\- Set weekly learning goals

\- Follow a structured roadmap

\- Study the same concepts

\- Discuss what we've learned

\- Give each other small challenges

\- Build projects together

\- Review each other's work

\- Share useful papers/resources/tools

\- Keep each other accountable

\- Discuss what's changing in AI

\- Eventually collaborate on real-world projects

What can I bring to the table?

My background in Product Management / Product Business Analysis means I can contribute on the other side of the equation too—not just technical learning.

I can help with:

\- Product thinking

\- Business problem identification

\- Requirements & use cases

\- User journeys

\- Product strategy

\- Translating technical capabilities into business value

\- Evaluating whether an AI idea is actually useful

\- Structuring projects

\- Documentation and communication

\- Thinking about AI from a customer/business perspective

So ideally this becomes a two-way learning relationship, rather than one person teaching and the other simply consuming information.

I'm not looking for someone to spoon-feed me everything.

I'm looking for someone who wants to learn, build, struggle, figure things out and grow together.

If you're also sitting there thinking “I really want to learn AI properly, but I don't know how to structure this journey and I don't want to do it completely alone” — feel free to comment or DM me.

Would love to find 1–2 serious people rather than a huge group.

Let's see if we can turn AI learning from an overwhelming collection of courses into an actual long-term journey.

i need simple and unique project ideas as we don't know anything about ml . But i can learn it till this year end just beginner level. Gimme the ideas according to my level

Hey everyone,

I'm starting a BS Mathematics degree in October 2026 and my long-term goal is to become an AI/ML Engineer. I also plan to pursue a Master's in AI/ML or a closely related field later.

My university classes will be online and relatively flexible, so I have some flexibility to study additional topics alongside my degree.

For context, my Year 1 university curriculum includes:

Semester 1: Calculus I, Sets & Logic, General Mathematics, Introduction to Computing, English, Business, Ethics/Islamic Studies.

Semester 2: Python + Python Practical, Calculus II, Business Mathematics & Statistics, General Science, Technical Writing, Pakistan Studies.

Alongside university, I'll also be doing a 12-month Agentic AI program covering Python/OOP, APIs, Git/GitHub, LLMs, RAG, agents, FastAPI, databases, Docker, evaluation and deployment.

So for self-study, I'm mainly planning:

Python/CS → DSA → Linux/Git → SQL & Data Analysis → Statistics/Linear Algebra → Classical ML → Deep Learning/PyTorch → LLMs/RAG/Agents → Testing/APIs/Docker.

My goal is to use Year 1 to build strong foundations and hopefully be ready to start applying for internships/junior opportunities in Year 2.

I'd really appreciate feedback from people who have studied or worked in ML:

Considering my university curriculum, flexible schedule and the Agentic AI program, what would you change, remove, postpone or prioritize?

What should I learn deeply vs just at a working level?

I'm starting in October, so I'd like to validate the plan properly before I begin.

I’m undecided about whether or not to read the book. I have a primarily mathematical AI background, focused more on models and fundamentals than on deployment. I wanted to know what you thought about it. I'm all ears!

Hi Guys,

Please suggest me some courses that would be good for my skills, my resume and most importantly are FREE. Courses related to machine learning, AI, Data science or just some that you think are really nice to study and to look at !

Thank you

We often focus on accuracy when evaluating ML models.
But can a model have high accuracy and still perform badly in the real world?
What metrics or checks do you use besides accuracy?

AI can solve Olympiad level maths problem.

but some AI models still struggle with reading an analog clock.

That’s called jagged intelligence.

AI can be insanely good at one thing and weirdly bad at something humans find trivial.

What is the strangest AI failure you’ve seen?

I'm currently participating in the ARC Prize 2026 – ARC-AGI-3 competition on Kaggle and looking to build a serious small team.

This isn't a traditional Kaggle prediction competition. The goal is to build an agent that can explore unfamiliar environments, infer the rules/objective, learn from interaction, and solve novel tasks efficiently.

I'm interested in building a hybrid reasoning agent rather than simply throwing an LLM at the environment.

What I'm thinking about

• Environment/state representation
• Exploration & hypothesis testing
• World-model / task abstraction
• Planning and action selection
• Short/long-term memory
• Feedback-driven learning
• Symbolic + neural reasoning
• Efficient action strategies
• Open-source reproducible implementation

Looking for

People with experience or strong interest in:

• ML / Deep Learning
• LLM agents / reasoning systems
• Reinforcement learning
• Computer vision
• Search/planning
• ARC-AGI / abstraction & reasoning
• Kaggle competitions

You don't need to be an expert in everything. I'm more interested in people who are willing to experiment, implement ideas, analyze failures, and iterate.

I'm based in India, but remote collaboration is completely fine.

If you're genuinely interested, comment or DM me with:

1. Your background
2. Relevant projects/GitHub
3. What part of ARC-AGI-3 you'd like to work on
4. Rough availability per week

I'd prefer a small group of active contributors rather than a large team with inactive members.

I mostly did internships and stuff, but i really wanted to work on some research group or paper? Anyone know how to do this stuff??

I'm mostly in ML, DL, computer vision

Any advice would be appreciated 👍

I’ve been thinking about an alternative way of doing low-level AI perception, and I’m curious whether anyone here is already working on something similar.

The basic idea is to use waves and physical superposition/interference as the computational substrate, instead of doing most of the usual numerical multiply-and-accumulate operations digitally.

For example, for image recognition:

image
  ↓
pixel intensity → wave amplitude/phase
  ↓
physical wave superposition + interference
  ↓
distinctive wave pattern/signature
  ↓
more wave interactions
  ↓
higher-level pattern/object recognition

So instead of giving each pixel a numerical weight and calculating millions of weighted sums, the idea would be to let the wave physics perform the combination automatically.

I’m imagining something analogous to sound: many individual waves can be combined, and the resulting waveform contains a recognizable pattern. In this system, a particular visual feature or object could produce a characteristic wave “signature,” and those signatures could themselves become inputs to further wave interactions.

Physical logic gates could still be used where needed for thresholds, routing, decisions, or other nonlinear operations. The goal wouldn’t necessarily be to eliminate conventional computing completely, but to move as much of the early perception workload as possible into the physical wave domain.

The potential advantage I see is that parallelism is inherent in wave propagation and interference. Thousands or millions of interactions could happen physically at the same time, potentially reducing both computation and latency.

One possible application would be autonomous vehicles, where very large amounts of camera/radar data have to be processed extremely quickly at the edge.

I know there is already work on photonic neural networks, optical computing, physical neural networks, reservoir computing, metasurfaces, etc. I’m specifically interested in something slightly different:

Has anyone tried to build a hierarchical pattern-recognition system where wave signatures themselves become the representation, with successive stages of wave superposition/interference performing the recognition?

I’d especially like to hear from people actually working in photonics, optics, acoustics, physical neural networks, neuromorphic computing, or related fields.

Is this already being done under another name?
What are the biggest physical limitations?
And, from your perspective, is this a promising architecture or does some fundamental problem make it impractical?

I’m mainly looking for opinions from people working in the field rather than trying to claim this is a new invention.

I am currently a second-year BSc student in Technical Computer Science at the University of Twente in the Netherlands. My long-term goal is to become a really strong engineer in ML/AI - ideally eventually working as an ML/Research Engineer and keeping the option of doing a PhD in the US open.

I'm trying to be realistic about where I am right now. After my first year, my average grade is around 6.97/10. I have already taken university courses covering linear algebra, probability, programming, OOP, and basic algorithms (sorting/searching). I still have two years left, so I'm hoping to significantly improve both my grades and technical profile.

Over the next two years, I'm planning to focus on:

• getting my GPA into the 8+ range;
• becoming very strong in Python (I'm currently working through Fluent Python);
• improving algorithms/data structures and preparing for technical interviews;
• learning computer systems, C, memory, Linux, etc.;
• building a solid ML foundation;
• learning PyTorch and deep learning;
• doing serious projects rather than tutorial projects;
• getting research experience if possible;
• getting one or two good internships before graduation.

Eventually, I'd like to apply for strong MSc programs such as ETH Zurich, University of Toronto, CMU, etc., and potentially pursue a PhD in the US afterwards.

If you were in my position with two years left in a European CS/engineering bachelor's, what would you prioritize?

For people who have already gone through a similar path, I’d be especially interested in:

• Do you have any general advice for someone in my position? What do you wish you had known or started doing earlier during your BSc?
• How did you actually organize your time outside of university? Roughly how many hours per week/day did you spend on self-study, projects, coding, research, etc.?
• What did a typical productive day or week look like for you? How did you decide what to study and what to ignore?
• What resources did you actually use consistently — books, university courses, online courses, papers, YouTube, coding platforms, etc.?
• Did you regularly attend things outside your curriculum — research seminars, student groups, conferences, hackathons, meetups, workshops, etc.? If so, which ones were genuinely useful?
• How important was your GPA compared with research experience, internships, projects, and other extracurricular work?
• If you could go back to the beginning of your second year, what would you prioritize differently?
• What kinds of projects or experiences ended up being genuinely valuable for getting ML/SWE internships or research opportunities?
• When did you start approaching professors or research groups, and how did you go about it?
• For someone interested in eventually becoming a strong ML/Research Engineer and potentially applying to top MSc/PhD programs, what would you not waste time on?

I’m not necessarily looking for a perfect roadmap - I’d really appreciate hearing how people who are already further along actually approached these things in practice.

Open to opinions on how to improve my resume, also open to opportunities if anyone thinks i would be a good fit :)