ML and Gen AI Certification

By the end you will have trained and evaluated a real machine learning model with scikit-learn, run and prompted an open LLM with Hugging Face Transformers, and fine-tuned that LLM on your own data using LoRA. You leave with working notebooks you keep, plus a clear sense of when to prompt, when to fine-tune, and when to train a model from scratch.

Yes. It is built for people who can read and edit simple code but have never trained a model end to end. That includes the software engineer who ships product code, the data analyst who is comfortable with data but new to model training, and the technical lead or PM who briefs ML and GenAI work but has never built it. You learn by doing.

No machine learning or data science background is needed. You only need enough Python to read and edit simple scripts. We walk through every ML and LLM step, from splitting data to fine-tuning with LoRA, so you are never staring at code with no context. The course starts from the fundamentals and builds up from there.

It fits a busy schedule. The hands-on build runs in a single focused half-day session, with everything done live in the browser, so there is no environment to wrestle with beforehand. After the session you keep the notebooks and the recording, which means you can revisit any step at your own pace whenever you have a spare hour.

It is a live, hands-on session where you build in Google Colab as you go, not watch slides. Everything runs in your browser with just a Google account, no local install and no GPU. You get the session recording afterward to revisit the steps and code, so if you cannot attend live you can still follow the full build at your own pace.

Yes, you keep it all. You leave with your own trained ML model and fine-tuned LLM in Colab notebooks that are yours to rerun and reuse on your own data.

Tutorials leave you watching someone else's screen. Here you actually train your own model and fine-tune your own LLM, with your hands on the code, and walk away with the working notebooks to prove it.

Yes. The hype around models keeps changing, but knowing how to actually train and evaluate one, run an open LLM, and fine-tune it with LoRA does not. These are the durable fundamentals underneath every new tool, plus a decision guide for prompt versus fine-tune versus train that keeps working no matter which model is in fashion this quarter.

Yes. Many learners expense this as professional development or upskilling. We can provide an invoice or receipt with the details your finance or learning and development team needs. If your manager wants the outcomes and format before approving, ask us and we will send a short summary they can sign off on.

During the session you build alongside the instructor with guidance at each step, so you are never stuck staring at code with no context. Afterward you keep the recording, your trained model and fine-tuned LLM notebooks, and the decision guide, so you can rerun everything on your own data and keep applying it well after the session ends.

Yes. We set aside a limited number of need-based scholarships for learners facing genuine financial hardship. If that is you, you can apply or learn more at theaiinternship.com/scholarship . We would rather the price of a seat not be the reason a capable person never gets to train their first model.

This is taught by Dr. Aki Wijesundara and Manu Jayawardana, who have trained 10,000+ students. It matters because the ML and GenAI material comes from practitioners who ship models for real clients, not a slides-only trainer. You learn the choices that hold up in real projects, like when to prompt, when to fine-tune, and when to train from scratch.

We want you here only if it is the right fit. If you complete the session and feel it did not deliver the hands-on build we promised, reach out and we will make it right. The surest way to know it fits is to ask us your questions before you enroll, and we will answer honestly about whether it suits your background.

You really train them. By the end you have a working ML model you trained and evaluated yourself and an LLM you fine-tuned on your own data using LoRA, both in notebooks you keep. Nothing here is a demo you watched the instructor run. The whole point is that your hands are on the code from start to finish.