Phase One: Orientation & Foundation (Weeks 1–4)

• Orientation & soft-skills training (communication, teamwork, time management)
• Basic industry-specific training in your chosen track
• Hands-on introductory activities (basic coding, task simulations)

Phase Two: Skill Development (Weeks 5–8)

• AI Engineering training — ML algorithms (regression, classification)
• Data preprocessing & visualization (Pandas, Matplotlib)
• Intro to TensorFlow/PyTorch for deep learning
• Software development essentials — OOP, system design basics
• Frontend (HTML, CSS, JS) and backend (Node.js, Django, Laravel) development

Phase Three: Collaborative Projects (Weeks 9–12)

• Agile methodology and version control with Git/GitHub
• AI model development & testing (linear regression, decision trees, neural networks — ANN, CNN, RNN)
• Model evaluation & optimization
• Collaborative software development project — APIs, databases, integrating AI models into software systems

Phase Four: Advanced Work & Career Entry (Weeks 13–16)

• Advanced deep learning (transfer learning, reinforcement learning)
• AI deployment (Flask for model serving, Docker for containerization)
• Final project combining AI and software development (e.g. an AI-powered web app)
• Portfolio building and interview preparation (CV writing, mock interviews)
• Present the final project to mentors and colleagues for feedback

Internship Outcome

• A capstone AI + software project, presented to mentors
• GitHub portfolio
• Industry-ready skills across AI and full-stack development
• Certificate + job placement support