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🎓 Global Skills Institute – Delhi
One-Year Professional Program in AI Ecosystem, Conversational AI & Agentic AI
📅 Detailed Weekly Plan
QUARTER 1 – AI Foundations & Ecosystem (Weeks 1–12)
Week 1 – Introduction to AI & Digital Transformation
- Evolution of AI (Rule-based → ML → DL → GenAI → Agentic AI)
Students understand how AI evolved from simple rule-based systems to autonomous decision-making agents. - AI in telecom, IT, aviation, HR, BFSI
Industry case studies show how AI improves operations, automation, and customer experience. - AI value chain
Covers data collection → model training → deployment → monitoring → business impact. - Lab: AI industry mapping exercise
Students identify AI opportunities in selected industries.a
Week 2 – AI Ecosystem & Market Landscape
- AI startups ecosystem
Overview of global AI companies, funding trends, and innovation clusters. - AI tools landscape
Introduction to ML frameworks, LLM tools, AI APIs, and cloud AI services. - Cloud AI platforms
Comparison of AWS, Azure, and GCP AI offerings. - AI adoption lifecycle
Steps enterprises follow to implement AI transformation. - Mini Project: AI Readiness Assessment
Students evaluate AI maturity of a company.
Week 3 – Python Programming Fundamentals
- Variables, loops, functions: Core programming constructs for writing AI code.
- Data structures: Lists, dictionaries, tuples for handling datasets.
- File handling: Reading/writing CSV, JSON data files.
- Lab coding exercises: Hands-on programming practice.
Week 4 – Python for Data Handling
- NumPy
Efficient numerical computations using arrays. - Pandas
DataFrame manipulation for structured datasets. - Data manipulation
Filtering, grouping, transformation.
Week 5 – Data Visualization
- Matplotlib
Basic plotting for data analysis. - Seaborn
Advanced statistical visualizations. - Dashboards
Creating interactive visual insights.
Week 6 – Git & APIs
- Git basics
Version control for managing AI code. - REST APIs
How AI systems communicate over web services. - JSON handling
Data exchange format used in AI applications.
Week 7 – Linear Algebra
- Vectors & Matrices
Foundation of ML computations. - Matrix multiplication
Core operation in neural networks.
Week 8 – Probability & Statistics
- Distributions
Normal, binomial, Poisson distributions. - Bayes theorem
Foundation of probabilistic models. - Hypothesis testing
Evaluating model assumptions.
Week 9 – Optimization
- Cost functions
Measuring prediction errors. - Gradient descent
Optimization method to minimize loss.
Week 10 – SQL & Data Engineering
- SQL queries
Extracting data from databases. - Joins
Combining multiple tables. - ETL basics
Extract, Transform, Load process.
Week 11 – Big Data & Cloud
- Hadoop overview
Distributed data storage framework. - Spark intro
Fast distributed computing engine.
Week 12 – Quarter 1 Capstone
Students design and implement a structured AI-ready data pipeline with presentation and viva.
QUARTER 2 – Core AI & Machine Learning (Weeks 13–24)
Week 13 – ML Introduction
- Types of ML (supervised, unsupervised, reinforcement)
- Model lifecycle from training to deployment
- Scikit-learn environment setup
Week 14 – Regression
- Linear regression for prediction
- Regularization to avoid overfitting
Week 15 – Classification
- Logistic regression for binary outcomes
- Decision trees for rule-based prediction
- Random forest for ensemble learning
Week 16 – Clustering
- K-means grouping algorithm
- Hierarchical clustering
Week 17 – Model Evaluation
- Accuracy, precision, recall
- ROC curve interpretation
Week 18 – ML Project
Students build full customer churn model including preprocessing, training, evaluation.
Week 19 – Neural Networks
- Perceptron model
- Backpropagation algorithm
- Activation functions
Week 20 – Deep Learning
- Building neural models
- Training & optimization
- Using TensorFlow / PyTorch
Week 21 – CNN
- Convolution layers for image recognition
- Feature extraction
- Transfer learning
Week 22 – RNN & LSTM
- Sequence modeling
- Time-series prediction
- NLP basics
Week 23 – NLP Basics
- Tokenization
- Word embeddings
- Sentiment analysis
Week 24 – Quarter 2 Capstone
Students build ML web app and deploy for real-world demonstration.
QUARTER 3 – Conversational & Generative AI (Weeks 25–36)
Week 25 – Transformers
Understanding attention mechanism powering modern LLMs.
Week 26 – Large Language Models
Prompt engineering, temperature control, fine-tuning concepts.
Week 27 – RAG
Embedding generation, vector databases, retrieval pipelines.
Week 28 – Conversational AI Design
Chatbot architecture and dialogue design.
Week 29 – Bot Platforms
Building bots using Dialogflow and API-based frameworks.
Week 30 – Context & Memory
Handling multi-turn conversations and maintaining context.
Week 31 – Generative AI
Content creation, AI-assisted coding, enterprise productivity.
Week 32 – Multimodal AI
Combining text, image, and audio models.
Week 33 – Enterprise AI Applications
Industry-specific AI use cases.
Week 34 – Ethical AI
Bias detection, hallucination control, AI guardrails.
Week 35 – Conversational AI Project
Enterprise chatbot development.
Week 36 – Quarter 3 Capstone
Multi-modal AI app presentation.
QUARTER 4 – Agentic AI & Deployment (Weeks 37–48)
Week 37 – Agentic AI
Autonomous systems capable of planning and acting independently.
Week 38 – Agent Architectures
Single vs multi-agent systems and reasoning models.
Week 39 – Agent Frameworks
Hands-on with LangGraph, CrewAI, AutoGPT.
Week 40 – Multi-Agent Collaboration
Designing cooperative AI agents.
Week 41 – Memory & Knowledge
Vector memory, long-term knowledge storage.
Week 42 – AI Workflow Automation
Enterprise automation using AI agents.
Week 43 – MLOps
Model packaging, monitoring, CI/CD.
Week 44 – Cloud Deployment
Scaling AI systems in AWS/Azure/GCP.
Week 45 – AI Governance
Responsible AI, regulations, risk management.
Week 46 – Productization
Building AI MVP and monetization strategy.
Week 47 – Final Capstone Build
Week 48 – Demo Day
🎯 Outcome
By end of 48 weeks, learners will:
- Build ML models
- Develop conversational AI systems
- Design Agentic AI architectures
- Deploy AI systems to cloud
- Create industry-ready AI portfolio
Final Industry Capstone (4 Weeks)
Students build one of the following:
- AI Enterprise Solution
- Conversational AI Platform
- Multi-Agent Autonomous System
- Industry-Specific AI (Telecom, Aviation, HR, Finance)
Final Presentation + Demo Day
Tools & Technologies Covered
- Python
- Scikit-learn
- TensorFlow / PyTorch
- OpenAI API
- LangChain
- LangGraph
- CrewAI
- Dialogflow
- Docker
- SQL
- Git
- AWS / Azure / GCP
🎓 Certification Tracks
- AI Practitioner
- Conversational AI Developer
- Agentic AI Architect
- Enterprise AI Consultant
📊 Evaluation Model
- 30% Labs
- 30% Projects
- 20% Capstones
- 10% Case Studies
- 10% Viva & Presentation
👨🏫 Delivery Model (Global Skills Institute – Delhi)
- Weekend + Evening Batches
- Corporate Batch Option
- Hybrid (Offline + Online)
- Industry Mentorship
- Placement Assistance
- Startup Incubation Support
📈 Career Outcomes
- AI Engineer
- Machine Learning Engineer
- Conversational AI Developer
- Agentic AI Developer
- AI Product Manager
- AI Consultant
- AI Automation Architect
🚀 Unique Differentiators
✔ Industry use-case driven
✔ Focus on Agentic AI (Next-gen AI)
✔ Enterprise deployment focus
✔ Multi-agent system training
✔ Real-world capstone projects
✔ Delhi-based industry connect