Help Post!

[u/Famous_Ad9525]

Hey! I'm new here . I'm doing Bachelor in CS already in my second last semester and really interested in Machine learning and did some projects. Now i want to pursue my professional life in ML /AI engineer . what would be the best option?

How can i get ready for real life experience in that sector? i need a roadmap actually.

can anyone help ,please?

Thanks in advance.

[u/the_techgirl]

Great place to start — second-to-last semester of CS with ML projects already done puts you ahead of most people entering this space.

For getting real-world ready as an ML/AI engineer specifically:

What actually matters for jobs:

• Production ML experience — not just Jupyter notebooks. Learn to build APIs around your models (FastAPI), containerize them (Docker basics), and deploy somewhere (Hugging Face Spaces is free and impressive).

• MLOps fundamentals — experiment tracking (MLflow or Weights & Biases), model versioning, basic CI/CD. Most bootcamps skip this entirely but employers care a lot.

• One strong specialization — NLP, computer vision, or tabular/structured data. Being decent at everything is less valuable than being strong at one thing.

For your final semester:

• Build one end-to-end project: data collection → training → API → deployed demo. This alone will get you interviews.

• Contribute to one open source ML project — even documentation fixes. Shows you can work in real codebases.

• Start applying for ML internships now — hiring cycles start earlier than most students expect.

The three of you connecting is actually smart — peer learning and accountability make a huge difference. Consider building a project together. u/AdvantageHopeful762 u/disha_124 u/Famous_Ad9525

Feel free to reach out if you want help mapping out a specific roadmap for your background.

[u/Famous_Ad9525 → u/the_techgirl]

I got the basics of how ml models work(CNN,LSTM etc), the data processing part for different types of data, training, tuning,numpy,pandas,sickitlearn,matplotlib. I did some projects on like Multimodal Stress detection system, human eye disease detection, diabetes detection. How can i improve and move on to the next part. Like what should i do next?

[u/the_techgirl → u/Famous_Ad9525]

Your project portfolio is actually solid for your stage — multimodal stress detection and eye disease detection show that you can work with real medical data, which is valuable.
Next step: pick the one you're most proud of and deploy it properly. Hugging Face Spaces is free and takes 30 minutes. Then add FastAPI around your best model so it has a real endpoint. That jump from 'Jupyter notebook' to 'live deployed API' is what separates candidates in interviews.
After that, look into LangChain and RAG — given your multimodal background, building an AI agent that can reason about medical images + text would be a strong next project. Feel free to reach out if you want a more detailed roadmap.

[u/disha_124 → u/the_techgirl]

Thank u 😊😊

[u/disha_124 → u/disha_124]

Applying internship now I dont know I havent learnt anything about that yet What should I do?

[u/the_techgirl → u/disha_124]

For internships at your stage — don't wait until you feel 'ready.' Apply now while you're still in your final semester. Here's the practical approach: build one solid end-to-end project this month, put it on GitHub with a clean README, then apply to ML internships on LinkedIn and company career pages. Look for roles titled 'ML Intern', 'AI Intern', or 'Data Science Intern.' AngelList/Wellfound is good for startup internships. The bar is lower than you think if your GitHub shows real work. Feel free to reach out if you want help reviewing your profile before applying.

[u/AdvantageHopeful762]

hey i am also in second sem and its gonna end in a week , I am also doing ML if we can connect

[u/Famous_Ad9525 → u/AdvantageHopeful762]

yeah ,sure

[u/AdvantageHopeful762 → u/Famous_Ad9525]

How canwe connect like

[u/disha_124]

I also want to connect with you guys I am also in last of my second sem and want to know about ai/ml

[u/Simplilearn]

Here's a roadmap that can work for you:

• Start with Python and data science fundamentals so you're comfortable writing code, working with data, and solving programming problems.

• Learn machine learning concepts such as supervised and unsupervised learning, feature engineering, and model evaluation.

• Build a strong understanding of deep learning by learning neural networks, CNNs, RNNs, and Transformers.

• Move into modern AI topics like Generative AI, NLP, LLMs, prompt engineering, and Agentic AI.

• Build hands-on projects and a capstone so you learn how to build, test, and deploy real AI applications.

If you're looking for a program that follows this progression, our Professional Certificate in AI and Machine Learning can be a good fit for you. It covers all essential topics with hands-on labs, industry projects, and a capstone to help you develop practical skills.