(1.5 hours/day → 135 hours).
PHASE 1 — AI foundation (Days 1–20).
PHASE 2 — Machine learning deep dive (Days 21–45).
PHASE 3 — NLP, CV & GEN Ai essentials (Days 46–65).
PHASE 4 — Special topics & responsible Ai (Days 66–80).
PHASE 5 —Projects & evaluation (Days 81–90).
(1.5 hours/day → 165 hours).
PHASE 1 — Python foundation (Days 1–20).
PHASE 2 — Python for data analysis (Days 21–40).
PHASE 3 — Data visualization (Days 41–55).
PHASE 4 — Statistics for data analytics (Days 56–75)
PHASE 5 — Sql for data analytics (Days 76–90)
PHASE 6 — Machine learning intro (Days 91–105)
PHASE 7 — Project and deployment (Days 106–110)
(1.5 hours/day → 270 hours).
PHASE 1 —Python + math foundations (Days 1–30).
PHASE 2 — Machine learning foundations (Days 31–60).
PHASE 3 — Deep learning (Days 61–90).
PHASE 4 — NLP + advanced deep learning (Days 91–120).
PHASE 5 — Gen Ai (LLMs) (Days 121–150).
PHASE 6 — AI agents & automation (Days 151–165).
PHASE 7 — ML/GENAI projects (Days 166–180).
Daily Duration: 1.5–2 hours
Mode: Theory + Labs + Case Studies + Weekly Assessments.
Daily Duration: 1.5–2 hours
Mode: Theory + Coding Practice + Mini Projects + Weekly Tests
Daily Duration: 1.5–2 hours
Mode: Theory + Tools Practice + Assignments + Live Projects
Duration: 1.5–2 hours/day Mode: Theory + Labs + Real Deployments + Weekly Tests Cloud Platforms Covered: AWS + Azure + GCP (Foundations of all, Deep focus on AWS)
Daily Duration: 1.5–2 hours/day
Mode: Theory + Figma Hands-on + Assignments + Projects + Portfolio Building
Daily Duration: 1.5–2 hours/day
Mode: Theory + Labs + Weekly Projects + Real-Time Full App Development
Stack Used: HTML, CSS, JavaScript, React, Node.js, Express, MongoDB, SQL, Git, DevOps Basics
Total Duration: 180 days (Approx. 6 months, 3–4 hours/day or adjustable)
Mode: Theory + Hands-on Practice + Mini-projects + Final Capstone Project
Goal: Make students industry-ready full-stack MERN developers
Duration: 90 days (3–4 hours/day recommended) Mode: Theory + Practical Labs + Mini Projects + Case Studies Goal: Enable learners to design, develop, and deploy monolithic and microservices-based applications.
Duration: 90 days (3–4 hours/day recommended) Mode: Theory + Hands-on Labs + Mini Projects + Portfolio Building Goal: Equip learners with skills in visual design, branding, UI/UX basics, and professional software tools.
Duration: 135 days (~4–5 hours/day recommended)
Mode: Theory + Practical Labs + Mini Projects + Capstone Project
Goal: Make learners industry-ready data engineers capable of handling large-scale data pipelines, processing, and analytics.
Duration: 113 days (~4–5 hours/day recommended) Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project Goal: Make learners industry-ready in DevOps practices, cloud-native development, CI/CD automation, and secure software delivery.
Duration: 180 days (~4–5 hours/day recommended) Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project Goal: Equip learners with skills to develop decentralized applications, smart contracts, and Web3 solutions using modern blockchain technologies.
Duration: 90 days (~3–4 hours/day recommended)
Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project
Goal: Equip learners with the knowledge and skills to build AR/VR applications and understand Metaverse concepts.
Duration: 113 days (~4–5 hours/day recommended)
Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project
Goal: Enable learners to design, develop, and deploy IoT and Edge AI solutions integrated with cloud services.
Duration: 45 days (~3–4 hours/day recommended)
Mode: Theory + Case Studies + Hands-on Exercises + Mini Projects
Goal: Enable learners to understand and implement ethical AI practices in real-world applications.
Duration: 120 days (~4–5 hours/day recommended)
Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project
Goal: Enable learners to design, develop, and deploy generative AI models for text, images, audio, and multimodal applications.
Duration: 105 days (~4–5 hours/day recommended) Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project Goal: Enable learners to design, implement, and deploy AI-driven automated workflows and intelligent agents for real-world business and technical applications.
Duration: 240 days (~4–5 hours/day recommended) Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project Goal: Enable learners to become industry-ready Data Scientists and MLOps Engineers capable of building, deploying, and maintaining ML workflows on cloud infrastructure.
Duration: 113 days (~4–5 hours/day recommended)
Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project
Goal: Enable learners to design, develop, and deploy cross-platform mobile applications using Flutter and Dart.
Duration: 113 days (~4–5 hours/day recommended)
Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project
Goal: Enable learners to leverage AI and ML techniques to detect, prevent, and respond to cyber threats effectively.
Duration: 113 days (~4–5 hours/day recommended)
Mode: Theory + Hands-on Labs + Mini Projects + Capstone Project
Goal: Enable learners to leverage AI and ML techniques to detect, prevent, and respond to cyber threats effectively.
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Discover decentralized systems, smart contracts, and Web3 applications.
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Understand fairness, transparency, and accountability in AI systems.
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