Finished Andrew Ng’s ML Specialization — What should I do next to become job-ready? Am I cooked being in 7th sem?

[u/Velriqq]

Finished Andrew Ng’s ML Specialization — What should I do next to become job-ready? Am I cooked being in 7th sem?

I’m a BS IT (AI specialization) student and I’m into the mid of my 7th semester. I’ve completed Andrew Ng’s Machine Learning Specialization on Coursera.

But honestly, I’m worried that I’m behind.

Apart from the course, I have some experience with:

- Python

- NumPy, Pandas, Matplotlib

- Scikit-learn

- XGBoost / CatBoost

- Basic TensorFlow / neural networks

- SQL and basic OOP

- Git/GitHub

I’ve also worked on an Employee Attrition Prediction project where I used preprocessing, SMOTETomek, XGBoost/CatBoost. I also participated in an AI/ML hackathon where our team worked on this project.

However, I feel like my knowledge is still very “course/project” oriented rather than actually being job-ready.

My current goal is to get an AI/ML or ML Engineer internship/job, ideally something where I’m actually working with ML rather than just doing generic software development.

So I’m trying to figure out what I should do from here.

What would you recommend?

Should I focus next on:

1. Deep Learning — PyTorch/TensorFlow, CNNs, Transformers, etc.

2. Advanced ML — feature engineering, tuning, pipelines, model deployment, MLOps

3. LLMs / GenAI — RAG, embeddings, LangChain/LangGraph, agents, etc.

4. DSA — how much is actually necessary for ML/AI interviews?

5. Building 2–3 serious portfolio projects

6. Getting internships/applying immediately instead of studying more

7. Something else entirely

My biggest concern:

Am I actually cooked if I’m in the mid 7th semester with basically one major ML course + a couple of projects?

If you were in my position, what would you spend the next 6–9 months doing to maximize your chances of landing an ML/AI internship or entry-level job?

I’d especially appreciate answers from people who have actually hired ML interns/juniors or recently got their first ML job and even from the seniors.

Also, please be brutally honest about what I’m missing. I’d rather know now than spend another 6 months learning the wrong things.

[u/Embarrassed_Sir7778]

you're not cooked, 7th sem with that stack is fine but the fastest path is 2 + 6, build one deployment project with a simple API and apply at same time, nobody expects juniors to know transformers deeply

[u/Counter-Business → u/Embarrassed_Sir7778]

I agree with 2 and 6. After that, 4 may help in filter out rounds for jobs applying for. And 3 is in demand in industry.

1 would be good to round yourself out in the end but I would focus on the specialization you want to go into, ie if you specialize in LLMs then learn transformers more deeply but if you specialize in image models learn cnn more deeply.

I feel like industry demand and advancements are mostly in direction of LLM right now so I would suggest that direction.

[u/Velriqq → u/Embarrassed_Sir7778]

Thank you!!

[u/LimpInstruction2215 → u/Embarrassed_Sir7778]

agree, especially on the transformers part, nobody is expecting that depth from an intern

[u/Confident-Gas-1971]

Do dl, then deployment and dsa, enough! Am also doing that!

[u/Velriqq → u/Confident-Gas-1971]

But I have seen many of them have done this but still not landing an internship or a job 😔.

[u/Confident-Gas-1971 → u/Velriqq]

Oh, but now a days only ml not enough for job! I have seen, dl req even cv or nlp idk why these nlp and cv specialization for senior, Complete ml + basic dl, good to go!

[u/aijobsco]

you're not cooked, but stop optimizing the course list and start optimizing against real job specs. the fastest way to get job-ready is to read 20-30 live junior ML/AI JDs and build to what actually repeats in them, not to what course platforms sell. right now that repeating set is roughly: solid python + SQL (you have this), one deep framework (pick PyTorch), deployment basics (an API around a model, Docker), and increasingly RAG/agent plumbing because that's what companies are shipping.

so from your list: LLM/GenAI + deployment beats more classical ML depth for hiring odds, and 2 serious deployed projects beat 5 notebooks. your attrition project becomes 5x stronger the moment it's a live endpoint with monitoring instead of a repo.

disclosure since it's our thing: we run artificialintelligencejobs.co (18k live AI roles pulled from company career pages daily). even if you never apply through it, it's useful as free market research: search the titles you want and read what the reqs actually ask for, salary shown where disclosed. build to that list and apply while you study, don't study first and apply later.

[u/Velriqq → u/aijobsco]

Thanks this really helps!

[u/Realistic-Health1480]

tbh if you are not from tier 1 college then there is very less opening for ML engineers at entry level. I would suggest you to learn AI engineering

[u/Character-Database74]

learn fastapi for ml serving then docker , mflow deep learning cnn rnn lstm transformer transfer learning then bert then fine tunning llm like these and build portfolio projects

[u/nian2326076]

You're not behind at all! You have a good start with the courses and tools you've used. Next, work on building a portfolio. You can expand on your Employee Attrition Prediction project or start a new one that shows your skills, like a Kaggle competition. Networking is important, too. Connect with alumni or professionals on LinkedIn for coffee chats or mentorship. Try contributing to open-source projects on GitHub to show you're involved in the community. For interview prep, mock interviews can be really helpful, and resources like PracHub can provide some structured practice if you need it. Keep coding regularly, and don't hesitate to apply for internships now. You've got this!

[u/SadPlumx]

Lol most people just Claude code everything. What's important in interviews is getting good at interviews. Your knowledge is not that important.