u/Exciting-Mud-1802

Into late night conversations, sarcasm, psychology, music, and people who can actually hold a conversation.

If you are interesting, slightly chaotic, or just avoiding sleep, message me.

22M from India

Good conversations are rare here, so trying my luck.

I usually connect better with mature people who can actually hold a conversation. Into music, psychology, random late night thoughts, and talking about literally anything for hours.

If you are sarcastic, honest, slightly chaotic, or just awake at odd hours, message me.

I’m a 3rd year Computer Engineering student and I genuinely can’t tell whether I’m actually decent at AI/ML or just building surface-level projects without deep understanding.

Right now, I’ve worked on:
- a RAG research project (published)
- an XGBoost box office prediction paper
- an emotion-based music recommender
- some NLP + transformer work
- LangChain / APIs / deployment stuff

But the more I learn, the more I feel like:
- I know tools more than fundamentals
- I can build projects but struggle with deeper theory
- I jump across topics too much
- college work leaves very little uninterrupted time to actually study ML properly

I want to stop doing “tutorial knowledge” and build strong foundations in:
- ML theory
- mathematics intuition
- model evaluation
- deep learning fundamentals
- research understanding

The problem is that AI/ML learning online feels extremely chaotic compared to something like learning Python from official docs.

So I wanted to ask people who are actually experienced in ML/AI:

1. What was the most structured learning path that worked for you?
2. Which resources actually made concepts “click” deeply?
3. Is implementing algorithms from scratch worth the time?
4. How do you balance theory vs building projects?
5. What should someone already doing projects/research focus on to become genuinely strong at ML?

Would appreciate brutally honest advice rather than motivation.

Hi everyone,
I’m a 3rd-year Computer Engineering student from India, and I’m planning my final-year capstone project. I want to build something that is industry-relevant and genuinely stands out, not a generic ML project.
A bit about my background:
CGPA: 3.68/4.0
2 published ML/AI research papers (IEEE WCSC 2026, DACS 2025)
Datathon Finalist (Analytika NMIMS)
Experience building end-to-end ML systems and evaluating models
Some of my work:
Multi-document RAG system with LLM evaluation and custom metrics
Box office revenue prediction using ML (XGBoost, feature engineering, tuning)
EmoSound: real-time mood-based music recommender using voice emotion detection
Tech stack: Python, Scikit-learn, TensorFlow, Transformers, LangChain, SQL, APIs (OpenAI, Spotify, etc.)

What I’m looking for:
I want to build a capstone project that:
solves a real-world problem
involves modern AI (LLMs, multi-modal systems, or advanced ML)
has scope for research or innovation (not just implementation)
can stand out for internships / grad applications

My question:
What are some industry-level project ideas or problem spaces you would recommend I explore?
It would really help if you could suggest:
specific problem statements
emerging areas worth exploring
or gaps you’ve seen in real-world systems

I’m open to ambitious ideas and willing to put in serious effort over the next 6–8 months.
Thanks in advance.

Hi everyone,
I’m currently a 3rd-year Computer Engineering student from India (NMIMS, Mumbai), graduating in May 2027, and I’m planning to pursue a Master’s in Data Science / AI / ML around July–August 2027.
A bit about my background:
CGPA: 3.68/4.0
2 ML/AI research papers (IEEE WCSC 2026, DACS 2025)
Datathon Finalist (Analytika NMIMS 2024)
Experience working on end-to-end ML pipelines and evaluation
Projects:
Multi-Document RAG System (research published, comparative analysis of LLMs with statistical evaluation)
Box Office Revenue Prediction using ML models (XGBoost with strong performance)
EmoSound – real-time mood-based music recommender using voice emotion detection (\~87% accuracy)
I’m comfortable with Python, ML frameworks (Scikit-learn, TensorFlow, Transformers), and currently deepening my knowledge in advanced ML and AI systems.

My interest in studying abroad started very early. My sister studied in the US, and I had the chance to visit it left a strong impression on me. Since then, pursuing a Master’s in the US has been a long-term goal.
However, recently I’ve been reconsidering due to factors like cost, competition, visa uncertainty, and overall ROI. Because of this, I’ve started exploring Australia as a more practical alternative.
At this point, I feel quite confused and want to make a well-informed decision instead of blindly following my initial plan.

My questions:
For MS in Data Science / AI / ML, how important is the GRE currently for top or good universities in the US? Is it still worth preparing for?
How do the US and Australia compare in terms of:
Quality of education
Job opportunities after graduation
ROI (cost vs career outcomes)
Given I have \~1–1.5 years, what should I focus on to strengthen my profile further? (research, internships, publications, etc.)

I would really appreciate insights from:
people currently pursuing their Master’s in the US or Australia
graduates working in AI/ML/Data Science
anyone who has recently gone through this process
I’m genuinely trying to make a smart, practical decision here and would value honest advice.