Curriculum
- 4 Sections
- 36 Lessons
- 90 Days
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- MODULE 1: Foundations of AI, Machine Learning, & Python Programming(Days 1–30 / Classes 1–12)12
- 1.1Class 1: Course Introduction & Roadmap
- 1.2Class 2: Fundamentals of Python for AI & Data Science
- 1.3Class 3: Advanced Python Programming & Libraries
- 1.4Class 4: Introduction to Artificial Intelligence
- 1.5Class 5: Machine Learning Fundamentals
- 1.6Class 6: Data Science Principles & Workflow
- 1.7Class 7: Exploratory Data Analysis (EDA)
- 1.8Class 8: Feature Engineering & Data Preparation
- 1.9Class 9: Supervised Machine Learning Algorithms
- 1.10Class 10: Unsupervised Learning & Clustering
- 1.11Class 11: Introduction to AI Agents & Automation
- 1.12Class 12: Applied Python Mini Projects & Review
- MODULE 2: Generative AI, Deep Q-Learning, LLMs, & Prompt Engineering(Days 31–60 / Classes 13–24)12
- 2.1Class 13: Introduction to Generative AI
- 2.2Class 14: Neural Networks & Deep Learning Basics
- 2.3Class 15: Large Language Models (LLMs): Structure & Function
- 2.4Class 16: Deep Q-Learning & Reinforcement Learning
- 2.5Class 17: Generative AI for Text, Images, & Code
- 2.6Class 18: Ethics of Generative AI & Responsible Use
- 2.7Class 19: Advanced Prompt Engineering
- 2.8Class 20: Fine-tuning LLMs for Custom Applications
- 2.9Class 21: Human-in-the-Loop AI & Crowdsourcing
- 2.10Class 22: AI Agents for Business Productivity
- 2.11Class 23: Generative AI Project Development
- 2.12Class 24: Midterm Capstone & Module Review
- MODULE 3: Blockchain, Data Science for Business, & Digital Marketing(Days 61–90 / Classes 25–36)12
- 3.1Class 25: Blockchain Technology Fundamentals
- 3.2Class 26: Smart Contracts & Decentralized Applications (DApps)
- 3.3Class 27: Data Science for Business Intelligence
- 3.4Class 28: AI-powered Automation in IT Management
- 3.5Class 29: Principles of Digital Marketing in Tech
- 3.6Class 30: Advanced Data Analysis & Visualization for Digital Marketing
- 3.7Class 31: Integrating AI and Blockchain for Business Solutions
- 3.8Class 32: Legal, Regulatory, & Ethical Issues in IT
- 3.9Class 33: Digital Transformation Strategies
- 3.10Class 34: Personal Branding & Career Readiness in Tech
- 3.11Class 35: Final Major Capstone Project (Integrated Tech Solution)
- 3.12Class 36: Graduation & Future Pathways
- ConclusionThis comprehensive, modular curriculum delivers a transformative learning journey across AI, generative models, machine learning, Python, blockchain, and digital marketing. Learners will graduate prepared to apply advanced technical and business skills, drive innovation, and lead in the tech industry. Each class leverages interactive activities, hands-on labs, and targeted learning objectives—ensuring students master critical concepts and real-world application for professional and career excellence.0
Class 16: Deep Q-Learning & Reinforcement Learning
Summary: Explore deep Q-learning, agent-environment interaction.
- Learning Objectives: Summarize RL concepts; train a simple agent.
- Key Topics: Rewards, policies, Q-tables, neural Q-learning.
- Activities: RL simulation, code walk-through.