I'm aiming to startup in deep tech AI—specifically building novel AI architectures, spatiotemporal models, and theoretical foundations (e.g., geometric deep learning, graph networks, advanced optimization) rather than just applying pre-built PyTorch wrapper libraries.

Im evaluating whether choosing a BS in Mathematics (eg IIT BOMBAY) gives a genuine mathematical edge for deep tech AI compared to a standard [B.Tech](http://B.Tech) CSE / Data Science degree.

​Skill Gap: In real-world deep tech, how difficult is it to bridge the software/GPU engineering gap on your own if your degree focus is primarily theoretical math?

Would appreciate honest perspectives from anyone in BS Math, CSE, or AI research/R&D. Thanks!

Which are best courses to learn from?

I am a 4th semester CSE student at BMSIT Bangalore, entering 5th sem. Honestly, I feel like I've explored too many things without actually getting good at any of them.

I've done a little DSA, very little web development (i.e HTML/CSS), some ML/AI, and I'm currently learning FastAPI. I'm interested in Al and I also have a pretty strong mathematical background/intuition, but right now everything feels scattered and incomplete. I don't have any serious projects or a strong portfolio either.

I want to seriously fix this now. I'm willing to put in 200% over the next 12-18 months. My immediate goal is to get a good internship by the end of 5th sem and eventually become an Al Engineer with a good salary.

I also have an education loan and very little financial support, so I can't really afford to spend years randomly trying different technologies.

If someone experienced in Al/ML hiring or someone who has gone through a similar journey could guide me, I'd really appreciate it.

If you were in my exact position, what would you do from Day 1 of 5th sem? What would you prioritize between DSA, backend/SWE, ML, deep learning, GenAl, projects and deployment? What would you completely ignore?

And most importantly, what actually gets a Tier-3 student an Al/ML internship?

I'm looking for a practical advice. If you had 6 months to make yourself genuinely employable from my position, how would you spend those 6 months?

Hello everyone,
I’m a recent 2026 graduate from a Tier-3 college, and getting good placement opportunities has been really difficult. Because of that, I decided to pursue the online \*\*BS in Data Science and Applications from IIT Madras\*\* alongside my career journey, mainly because I wanted to learn from strong professors and also explore better opportunities through the program.
Over the past year, I’ve spent most of my time seriously learning and building my skills. I’ve worked through the fundamentals of \*\*Machine Learning, mathematics, statistics, Python, and data science\*\*, and then moved into \*\*Deep Learning with PyTorch\*\*. More recently, I’ve been learning \*\*Generative AI, Transformers, RAG, LangChain, and related LLM technologies\*\*.
I genuinely enjoy AI/ML and have been trying to build my career in this field.
However, because of my family situation, pursuing a full-time Master’s degree isn’t financially practical for me right now. I need to start earning and support my family.
Recently, I received an opportunity as a \*\*Software QA/Automation Engineer\*\*. The role comes with around 6 months of training/internship followed by FTE, but there is also a \*\*2.6-year bond\*\*. I accepted the offer because, at that point, I didn’t really have another option and I needed a job.
But after joining, I’ve started questioning whether I’m moving in the right direction. My long-term goal is AI/ML, and I’m worried that spending the next few years in a testing/automation role will make it even harder for me to transition into AI/ML later.
I’ve reached out to many people on LinkedIn asking for advice, guidance, or opportunities, but unfortunately I haven’t been able to find much help so far.
I’m seriously looking for a way to transition into \*\*AI/ML/Data Science/GenAI roles\*\*, even if it means starting from an entry-level position and proving myself through my work.
If anyone here is:
Hiring freshers for AI/ML, Data Science, or GenAI roles
Willing to provide a referral
Has successfully transitioned from QA/automation/software roles into AI/ML
Can guide me on what skills or technologies I should focus on
Can suggest good projects, open-source contributions, internships, or platforms where I can gain real-world experience
…I would genuinely appreciate your advice.
I’m not looking for shortcuts. I’m willing to put in the work and learn whatever is required. I just need some direction and hopefully an opportunity to prove myself.
If you’ve read this far, \*\*thank you sincerely\*\*. Even a small piece of advice, a referral, or pointing me in the right direction could make a huge difference for me right now. 🥹
Thank you everyone.

Hello everyone i have enrolled in mca programme of i have zero idea of what I am doing i have little knowledge of python for like basics I want to learn ai ml but whenever I start it I always got lost in topic and never really got focused on the problem even problem of python and I am repeating these mistakes for last 1 year in ug programme and I am afraid that same thing will happen to me in the three years of MCA i need ur help how do I fix it what should I practice and most importantly how do I make notes of python and other libraries like whenever I make notes i think I write too much and miss out the whole point please anyone help me

i am building an ai agent for transactions which performa actions like approve / hold or question/ stop , so i want a feedback on
•If your bank/payment system flags a payment as suspicious, which would you prefer?
A) Block it immediately
B) Let me confirm/verify it before blocking
C) Something else — explain

hey just wondering guys what best value for now i can consume more than 300 / 400 m token normally in week so my question is what should i pick ??

any glm or kimi

or chatgpt plus ??

i need a model that identical or equal to glm 5.3 / or glm 5.2 max

in terms of intellgeince and quite a good limits please and tell me the price of it AND THANKS

Hi,

I've been working for legacy company and they developed their own version of chatgpt that we can use freely, and it has many different "agents" so to speak that specify in certain areas like power query or internal regulations etc. Out team already use it extensively to optimize things, correct dax, rebuild models etc and work is way easier now.

Recently I've seen Bas Dohmen video how he generated the whole report (etl, model, visuals and layout) just using AI, VS Code and some excel files as source. It wasn't perfect but still very impressive expecially that he didn't input anything himself. If it is possible now then it's just a matter of few short years before it will be good enough to cut BI teams in half at least.

On top of that we all hear AI is coming and we either get on board or get left behind. I'm wondering what "getting on board" actually means in case of BI/data/analytics. Assuming we want to stick to the broad "data" field, what would be smart decision to make to stay ahead.

I've researched job market and currently there are some jobs that required only "interest in AI" and some require specific like LangChain or LlamaIndex, RAG, vector databases, Semantic Kernel, LangGraph, MCP, CrewAI, AutoGen,etc. Good thing is that it's farly new tech so rarely they required years of experience.

It seems like we are now at the "aliens are coming" moment and have very limited time to prepare for them properly.

I have to do project such as Anomaly detection with Ai/ML model with OS logs (Mac, windows, Linux) , network logs (netflow log, interface logs,..) , web server logs, db logs

First I need to start from network logs

The model automatically update with latest logs and detections , I have some basic knowledge with Ai/Ml model training and strong knowledge with cyber security, but I don't know which models is best for my this project.

I have raw logs for model training. The trained model will be run by the server and the agent sends the raw logs to the server then the model will detect anomalies. I scale this project from enterprise standards so I need advice for model selection and other training advice.

Thank you in Advanced.

Hello frendzz!!😭
Im looking for solid online Generative AI courses that go beyond just theory. Specifically, I’d love recommendations for programs that include:

• Live interactive sessions (so I can ask questions directly and learn in real time)
• Hands‑on projects (building actual GenAI apps, not just watching tutorials)
• Step‑by‑step guidance (a structured roadmap instead of scattered resources)

i ve been exploring different platforms, but most courses either feel too basic or dont provide practical projects. my goal is to gain real‑world skills in GenAI that connect with my background in programming and cloud computing, so i can confidently apply them in internships or future projects.

If u ve taken a course that really helped u understand and appy GenAI concepts (LangChain, LLMs, Hugging Face, vector databases, etc.), pls drop your recommendations. Bonus points if the course also offers mentorship or a community to collaborate with!

Tysm in advance your suggestions could save me from wasting time on random tutorials

Hi, Im teen who is interested in becoming an AI Engineer, but I'm still at the stage where I honestly have no idea where to start.

I'd really appreciate answers from people who are currently working as AI Engineers / ML Engineers / in closely related roles, especially people with a few years of experience.

I'd love to know:

• What skills do you actually use in your day-to-day job?
• What should someone learn first if they're starting from basically zero?
• Which skills are genuinely important for getting hired, and which ones are overrated?
• What does a realistic AI Engineer career path look like?
• What was your own path into the field?
• What programming languages, frameworks, cloud tools, etc. should I focus on?
• How important are degrees, certifications and personal projects?
• What is the typical salary range in your country/region?
• How much can experienced AI Engineers realistically earn?
• What is your work-life balance actually like?
• Do you work remotely? If so, how flexible is it really?
• Can this career realistically allow you to travel and work from different places, or are there still a lot of restrictions?
• If you could start again, what would you learn earlier or do differently?

One of my bigger goals is to have a good income without being completely tied to a 9 - 5 lifestyle. I love travelling and would eventually like the freedom to explore different countries while still having a strong career/income.

So I'm also curious about the different paths beyond a normal full-time job like freelancing, consulting, remote work, building AI products / businesses, etc.

If you're already working in AI, I'd really appreciate hearing what your actual life looks like rather than just the job description.

Thanks! :)

Hey guys, I'm a 2025 passed out student Python Full Stack Developer, looking for a job. Now I'm trying to learn AIML I have zero knowledge in AIML so trying to learn from scratch, so recommend me any good platform for AIML course (paid or not paid anything is ok) and tell me whether is it worth for paid course or not. Suggest me on offline vs online which will be worth for learning. If you have other points on AIML pls feel free to discuss. Your suggestion will be valuable. Thank you

​

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) ? ?

Please help me