Learn prompt engineering and advanced generative AI, then add the machine learning techniques companies use every day: modeling, clustering and time series forecasting. Build projects where the two work together.
Prompt engineering comes first, so you work with AI models from week one. Machine learning follows, then generative AI applications, then deployment and job preparation.
The model finds the pattern. The LLM explains it, names it or acts on it. You learn both halves.
Build the forecast with Prophet or ARIMA, then have an LLM write the plain-English summary and answer questions about it. Managers get a report instead of a chart.
Group customers with K-Means or DBSCAN, then use prompt chains to name each segment, describe it and draft campaign ideas. Marketing teams can act on it straight away.
Train a classification or regression model, then retrieve from business documents to explain each prediction and suggest next steps.
Each project is reviewed by an industry mentor before it goes on your GitHub.
A RAG chatbot that answers questions from company documents and cites its sources.
A time series demand forecast with an LLM layer that writes the insight report and answers follow-up questions.
K-Means and DBSCAN clusters that an LLM names, describes and turns into campaign ideas.
A multi-tool AI agent that completes business workflows, deployed as an API with FastAPI and Docker.
Labs run in our cloud sandbox, so a standard laptop and a browser are all you need.
Our alumni work at Amazon, Microsoft, Deloitte, PwC, Cognizant and Tata Consultancy Services. Read their stories →
Not sure the program fits your background? Talk to an advisor.
Basic Python helps, and a short refresher runs alongside the first weeks. The machine learning techniques (modeling, clustering and time series forecasting) are taught from scratch, so you don't need prior ML knowledge.
Prompting and LLM applications are powerful, but many business problems still need classic techniques like forecasting, segmentation and prediction. Learning both lets you pick the right tool, and combine them, for example by having an LLM explain a forecast in plain English.
We cover the major model APIs (OpenAI, Anthropic Claude and Google Gemini) plus open-source models through Hugging Face, along with LangChain, LangGraph, vector databases, scikit-learn and Prophet. Because this field moves quickly, the curriculum is reviewed for every cohort.
No. Labs run in our cloud sandbox, so a standard laptop with a browser is enough. Sessions are live and online, and every lecture is recorded and available on your student portal with lifetime access.
The Data Science program is broader on analytics: Python, SQL, R, Looker Studio, BI dashboards and big data. This program focuses on prompt engineering and advanced generative AI, plus the ML techniques that pair with them. Many learners do both.
Yes. This program is covered by our written Placement Guarantee Agreement: a 180-day placement window, a minimum of 5 guaranteed interviews, and a 100% tuition refund if you meet the milestones and no offer arrives. Read the full terms on the Placement Guarantee page.
