AI, ML & GenAI landscape: Build a simple mental map of the ecosystem so learners understand what these domains are, how they differ, and what kinds of roles and use cases exist across them.

Developer setup & Python foundations: Learn the practical setup needed to start working in the domain along with the core Python required for data and AI workflows—variables, types, control flow, functions, files, and notebook discipline.

Data analysis with spreadsheets, SQL & pandas: Learn how to inspect, clean, query, and summarize tabular data with focus on filters, pivots, joins, groupby, missing values, and turning raw data into usable information.

Visualization, EDA & business thinking: Learn how to read data properly, spot patterns and anomalies, create clear charts, and connect a dataset to business questions such as funnel, conversion, retention, and performance.

APIs & mini analysis build: Learn how modern tools consume external data through APIs and JSON, and apply the full flow—ingest, clean, analyze, and present insights—through one small project.

ML workflow & problem framing: Understand the machine learning workflow end-to-end—problem framing, train/validation/test split, baselines, cross-validation, and choosing the right metric for the right problem.

Data preparation for ML: Learn the preprocessing habits that make models work in practice— imputation, encoding, scaling, leakage prevention, simple feature engineering, and handling imbalance.

Regression & classification: Learn the most important beginner ML modeling patterns through Linear Regression, Logistic Regression, Decision Trees, and Random Forests, along with practical model evaluation and error analysis.

Clustering, model selection & explainability: Get exposure to unsupervised learning, learn how to compare models sensibly, and build intuition for overfitting, generalization, and explaining outputs simply.

LLM fundamentals & prompting: Understand how LLMs work at a practical level—tokens, context windows, temperature, latency, cost—and learn prompting patterns that improve output quality and reliability.

Structured outputs & applied AI workflows: Learn how to make LLMs produce usable outputs and build practical workflows such as extraction tools, summarizers, FAQ bots, and CSV copilots.

RAG foundations: Learn the core idea behind retrieval-augmented generation—embeddings, chunking, retrievers, vector stores, semantic search—and build a grounded document assistant. Tools: ChromaDB / FAISS, sentence-transformers / OpenAI embeddings

Agents 101: Understand what makes an agent different from a prompt by learning tool use, state, retries, hard stops, and small multi-step action loops.

Agents + RAG + evaluation: Combine retrieval with agentic patterns, and learn how to evaluate quality, reduce hallucinations, add guardrails, and think responsibly about privacy and reliability.

Everyday English for Self-Expression
Learn how to talk about yourself comfortably and confidently in common situations.

Building Clear and Correct Sentences
Strengthen sentence formation so your English sounds natural and complete.

Speaking with Impact and Awareness
Learn to organise thoughts, speak smoothly, and communicate with confidence.

Professional Communication & Soft Skills
Apply spoken English skills to professional and workplace-like situations.