Courses Descriptions

🗂️ 18-Month Module-wise Breakdown

Phase 1: Foundations (Months 1–3)

• Programming Basics: Python, JavaScript

• Software Engineering Principles

• Git/GitHub & Agile Methodologies

• Introduction to Databases (SQL/NoSQL)

• Math for AI (Linear Algebra, Probability, Statistics)

Phase 2: Core Development (Months 4–6)

• Object-Oriented Programming (OOP)

• Full-Stack Development Basics (HTML, CSS, JS, Node.js/React)

• API Development & Integration

• Database Design & Optimization

• Cloud Basics (AWS/GCP/Azure)

Phase 3: AI & ML Foundations (Months 7–9)

• Python for AI (NumPy, Pandas, Matplotlib)

• Machine Learning Fundamentals (Regression, Classification, Clustering)

• Neural Networks & Deep Learning Basics (TensorFlow, PyTorch)

• Data Preprocessing & Feature Engineering

• Hands-on ML Projects (Predictive Models, Recommendation Engines)

Phase 4: Advanced AI Development (Months 10–12)

• Natural Language Processing (NLP)

• Computer Vision (CV) Applications

• Generative AI (LLMs, Transformers, GANs)

• AI for Software Development (GitHub Copilot, Code Assistants)

• Capstone 1: AI-powered Web App

Phase 5: Real-World Applications (Months 13–15)

• AI in Cloud & DevOps (MLOps, CI/CD pipelines for AI)

• Scalable Software Systems with AI Integration

• AI Ethics, Bias, and Responsible AI Practices

• Enterprise AI Case Studies

• Capstone 2: Enterprise AI Solution

Phase 6: Career Readiness & Specialization (Months 16–18)

• Specializations: Choose Data Science, AI Engineering, or AI Product Development

• Interview Prep: DSA, System Design, AI problem-solving

• Portfolio Building (GitHub, LinkedIn projects, blogs)

• Mock Interviews with Industry Mentors

• Final Capstone: AI-driven Product Launch

🎯 Outcomes

• Full-stack developer skills with AI integration

• Proficiency in ML, NLP, CV, and Generative AI

• Cloud & DevOps practices for AI systems (MLOps)

• 3 Capstone projects + strong portfolio

• Industry readiness for roles like AI Engineer, Software Developer – AI, ML Developer, or Data Scientist