Calendar

This session walks through a full ML workflow—from using pretrained models to training with labeled data—using tools like Data-to-Science, Jupyter, PyTorch, GitHub, and Huggingface.

This session explores how LLMs interact with external tools and APIs, including function calling, Text-to-SQL generation, and schema handling using RAG, with practical database applications.

Understanding fundamental AI and ML concepts, terminology, and the machine learning workflow.

A session designed to enhance team communication and ensure research objectives are clearly defined and effectively communicated through structured planning processes.

Implementing basic supervised and unsupervised learning models using Scikit-learn.

Introduction to text data processing, feature extraction, and simple NLP tasks.

Exploring how Large Language Models (LLMs) can assist in data cleaning, generating insights, and even writing Python code for analysis.

A session focused on mastering project tracking from start to finish using proven methodologies, AI tools, and best practices for effective meetings and milestone management.

Integrating skills from previous sessions to complete a mini-project, from data ingestion to insight generation with an AI component.