AI Resource Bootcamp
An enriching, multi day workshop organized by Jain Institute of Engineering & Technology, designed to provide students with practical, hands on experience in modern AI, automation, and deployment tools. Led by key speaker
Training
Services
AI Education & Training
Category
AI Resource Training
Client
Jain Institute of Engineering & Technology
Day 1: Exploring AI Tools and Platforms
The first day introduced participants to powerful AI models and development tools.
AI Models: We explored advanced AI models like Gemini 2.5 Pro, ChatGPT, and GPT-4.0 for building intelligent systems and for their advanced reasoning capabilities.
Cloud & Development Tools: We learned to use Firebase and Google Cloud Free APIs , and ran Python code on Google Colab. We also covered CDN integration and deployment tools such as Vercel, Cloudflare, and AWS.
AI for Development: The session provided hands-on exposure to AI agents like Tactacgo and Cursor AI Agent GPT-4O-mini. We also used ClaudeAI to generate most of the code for website development.
Day 2: Portfolio Development & Deployment
This session focused on the essential steps for creating a professional online presence.
AI-Powered Website Building: We used VO (Vzero), an AI-powered tool that simplifies website development by providing AI-generated UI components and layout suggestions.
Deployment Workflow: After building our portfolios, we learned to deploy them using Vercel. The process included configuring the project, connecting the repository, and hosting the website with a live link.
Version Control: We were also guided to push our projects to a GitHub repository to maintain version control and share our work with potential employers.
Day 3 & 4: Automation, NLP, and Machine Learning
We explored tools for automating workflows and delved into core concepts of data science and AI.
Automation: We learned to automate workflows using Make.com and explored the capabilities of Zapier and Descot for repetitive tasks.
Natural Language Processing (NLP): Key NLP concepts covered included text normalization, tokenization, stemming, lemmatization, and Named Entity Recognition (NER).
Machine Learning Workflow: The session detailed the complete ML workflow, from data collection and feature engineering to model training and evaluation.
Day 5: Data Science & Model Training
The final day provided an in-depth understanding of how data is processed and how models are trained in real-world scenarios.
Python Libraries: We were introduced to essential Python libraries like Pandas, NumPy, and Scikit-learn for data manipulation and model development.
Real-World Projects: The session included a valuable demonstration of five real-time projects. We learned to load datasets, process data, train models, and evaluate performance using classification and regression algorithms.
Algorithms Covered: We covered fundamental ML algorithms such as Linear Regression, Decision Trees, and Naïve Bayes.
Conclusion
The AI Resource Bootcamp was an enriching and practical experience that provided a strong foundation in modern AI, automation, and deployment tools. Participants particularly appreciated the focus on real-world applications and the opportunity to gain hands-on experience by building and deploying a portfolio website.
