Introducing TEDSphere: What is #TEDSphere?

Our AI/ML Bootcamp is part of our new TEDSphere Learning Series, an education initiative designed to offer structured, skill-focused programs across areas such as artificial intelligence, machine learning, mobile development, web development, and cloud technologies. TEDSphere represents our long-term learning ecosystem - a continuous platform for students to grow through bootcamps, hands-on workshops, and technical training sessions, all under one unified series.

What is AI/ML Bootcamp? What should I expect?

As the first program within this framework, the AI/ML Bootcamp spans four weeks, and each week includes two sessions: one theory-based session to build the conceptual foundation, and one interactive workshop to apply concepts in real time using modern tools and Google technologies. Each week, participants receive homework assignments through Google Classroom, and both attendance and assignment performance play a key role in tracking progress. Also our bootcamp is open for external participants so people from other universities or different backgrounds are also welcome to attend. Those who successfully complete the program and meet the participation requirements will receive a Certificate of Participation at the end of the bootcamp.

Week 1: Introduction to AI and ML - Theory Session

During the first week of our AI/ML bootcamp, we introduced participants to the foundations of artificial intelligence, machine learning, and deep learning, and discussed how these technologies are used across different industries. We covered the differences between AI, ML, DL, NLP, and computer vision, explored real-world applications, and walked through core data concepts such as data types, preprocessing, feature engineering, normalization, and handling imbalanced datasets. We also explained basic ML workflows, including supervised and unsupervised learning, training–test splits, and evaluation logic. Throughout the session, we highlighted how Google technologies fit into this ecosystem by referencing tools like Gemini for reasoning and prototyping, TensorFlow for deep learning pipelines, and NotebookLM as an emerging tool for research support and workflow assistance. By the end of the class, participants gained a clear conceptual foundation to prepare them for hands-on model development