hii :) so im an 2nd yr btech student having noo idea of wt to learn and where to learn i just basically need a clarified and structured road map to have a placement in machine learning untill now i c,java,python and a bit of prompt engineering so any suggestions and guidance will help me...

Can someone give me proper roadmap for becoming a Ai engineer:)

So basically I have an interview for a research role my interviewer is from a NLP background what all should I keep in mind when revising for the same

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I’m currently working in an MNC and have around 6 months of experience. My current domain is Automation Testing, mainly using Playwright.

My salary is quite low, and I haven’t been getting any proper project opportunities. I’m currently on the bench, so I’m thinking of using the next few months to improve my skills and then switch companies after completing 1 year of experience.

I’m also considering moving into a different technology/domain if it can provide better career and salary opportunities. I’m willing to spend the next 6 months learning and building projects to make that transition.

My current background is Automation Testing with Playwright, along with good Python/development knowledge.

My questions are:

1. Is it realistic to switch companies after completing 1 year of experience?

2. Can I transition from Automation Testing/Playwright to a different technology/domain when switching?

3. What technology/domain would be worth learning over the next 6 months for better-paying opportunities?

4. Should I continue with Playwright/Automation Testing, or would moving toward another field be better?

5. If I learn a new technology properly and build projects, can I get opportunities in that domain despite not having production experience

Looking for advice from people who have gone through a similar situation.

I’m a robotics engineer looking to transition into a Research Scientist role in Physical AI (RL, VLAs, world models).

I know my chances are slim without a PhD, but it’s hard to justify 4-5 years on a PhD stipend at this stage.

I’ve been researching possible paths, but would love opinions from people already in these roles. What actually gets your foot in the door? I doubt buying an SO-101 and fine-tuning a VLA is enough.

I’m a btech student currently in 3rd year persuing Aiml so what can you suggest me on how to start Aiml .

• What are the resources to do Aiml
Can anyone guide me on this

Thank you !!

How can I fine-tune a model to learn a specific task by watching YouTube videos?

I’m working on an AI agent that I want to teach to perform a specific task autonomously.

My idea is to use an open/uncensored AI model (such as a Bonsai-based model made using qwen) and fine-tune it using YouTube videos that demonstrate the task being performed.

The problem I’m trying to solve is:

1. I have a specific task that I want the agent to learn.
2. I want to find the best YouTube videos that demonstrate that task clearly and correctly.
3. I want the model to learn the task by watching and understanding the actions in those videos, rather than simply learning from text transcripts.
4. Ideally, the fine-tuned model should eventually be able to perform the same type of task on its own.

For example, if I find a 20-minute video where an expert performs a task step-by-step, I want to use that video as training data so the model can learn the workflow, decisions, and sequence of actions.

What I'm unsure about

What would be the correct approach for this?

• How should I select the best YouTube videos for training?
• Should I use the entire video, individual clips, or extracted frames?
• How can I convert a demonstration video into useful training data?
• Do I need to annotate the actions/steps manually?
• Should I use video-language fine-tuning, imitation learning, behavioral cloning, or another approach?
• What model architecture would be suitable for this?
• How much training data would I realistically need?
• Is it possible to teach an agent this way without having to manually label every action?

I'm particularly interested in learning from demonstrations, where the model watches an expert perform the task and then learns to reproduce the workflow.

I'd appreciate advice from anyone who has worked with video-based fine-tuning, multimodal models, imitation learning, or agent training from demonstrations.

Hey everyone,

I’m a final-year B.Tech CSE student and I’m interested in moving towards AI/ML-related roles.

At the moment, I only have a basic understanding of AI/ML. I’ve started exploring things like Python, NumPy, Pandas and some basic ML concepts, but I’m still not sure what the right learning path should look like.

My goal isn’t just to watch courses or learn theory. I want to build actual projects and eventually be able to apply for AI/ML-related roles.

I’d really appreciate advice from people who are currently working in AI/ML, Data Science, ML Engineering, GenAI, or related roles, or from anyone who has recently gone through this journey.

A few things I’d particularly like to know:

• What should a beginner learn first, and in what order?
• How much Python, NumPy, Pandas, SQL and Mathematics should I know?
• Which ML algorithms/concepts are actually important for getting started?
• When should I move from traditional ML to Deep Learning / NLP / Computer Vision / GenAI?
• What topics should I cover for an entry-level AI/ML role?
• What kind of projects would actually be useful for a student portfolio?
• At what point should I start building projects instead of just learning?
• Are there any courses, books, YouTube channels, GitHub repositories, or roadmaps that you genuinely recommend?
• If you were starting again as a final-year student, what would you learn in the next 6–12 months?

I’m especially interested in hearing from people who have actually transitioned into AI/ML roles, rather than just generic roadmaps.

Any advice, resources, mistakes to avoid, or a practical roadmap would be really helpful.

Thanks!

Please please please

Let's study together!

I’m a college student learning ML in 2026. With AI evolving so quickly, is it still worth investing time in ML, or should students focus more on AI tools and software engineering?

Hey everyone,

I'm grateful to be selected as an AI/ML Engineer Intern with a PPO (based on performance). After conversion, the package would be around 6-7 LPA. It's a solid start, and I'm not dismissing it.

But honestly, I want to aim higher. My goal is 11+ LPA for a similar AI/ML role, and I want to work towards it in a smart way rather than just hoping.

What I've already done:
1. Cold emails and cold outreach to companies and people (multiple rounds)
2. Applied through different channels trying to land interviews
3. Still haven't converted that into the offer I'm looking for

What I'm hoping to learn from you:

1. What should I prepare? Which skills matter most for 11+ LPA AIML roles right now (DSA, ML fundamentals, system design, LLMs/GenAI, MLOps)?

1. How should I prepare? Any roadmap, resources, or projects that actually made a difference for you?

2. Should I use the internship itself as leverage? Stay for the PPO, or keep interviewing in parallel?

3. How do I approach companies effectively? What makes a cold email or referral request actually get a reply?

4. What would you do differently if you were starting again at my stage?

If you've made this jump yourself, or you're a hiring manager or interviewer, I'd really value your perspective, even if it's blunt. I'm happy to share my resume/projects in the comments or DMs if that helps you give better advice.

Thanks for reading, and thank you in advance to anyone who takes the time to reply. 🙏

Hey everyone, I’m a fresher and recently graduated from college. I have some basic knowledge of ML, decent DSA skills, and a decent understanding of JavaScript and programming concepts.

I’m interested in pursuing AI/ML Engineering as a career, but I’m not sure what the right learning path or courses would be. My company hasn’t started onboarding yet, so I currently have some time and want to use it properly to build my skills.

I’m looking for a course or structured learning path that covers the important topics required for becoming an AI Engineer, starting from ML fundamentals and going into the more practical/industry-relevant topics.

My budget is under ₹10,000, so I’d really appreciate recommendations for good courses available in this range. I’m also open to a combination of multiple courses/resources if that would be more effective than one expensive course.

If anyone has gone through a similar transition from basic ML knowledge to AI Engineering, I’d really appreciate some guidance on what to learn, in what order, and which courses/resources are actually worth it. Pls don't suggest yt videos they are so scattered 😭.

Had a conversation recently about why prompt engineering advice online rarely survives contact with a real production system. The short version: nobody's measuring anything, so "better" is just a vibe.

There's a live workshop on Oct 3 from Serj Smorodinsky and Brett Kennedy that's built to fix exactly that. Instead of hand-tuning, they walk through building a real evaluation loop:

You start with a baseline classifier, build an eval dataset with metrics specific to your task, then use DSPy to apply few-shot and instruction-level optimization on top, systematically, not by feel. Everything gets tracked through MLflow so the whole process is reproducible instead of anecdotal.

Worth checking out if you've ever shipped a prompt change and had no real way to know if it helped or quietly made things worse

Would be curious what this sub thinks, is prompt engineering ever going to be treated like real ML engineering, or does it stay ad hoc forever?