Final Capstone Project: Designing and Deploying a Complete AI System
web-development.

Course Modules:
Module 1: Project Selection & Planning
Choose your domain: NLP, computer vision, RL, predictive analytics, etc.
Define a problem statement and target outcome
Set milestones, tools, and success metrics
Module 2: Data Collection & Preparation
Identify or collect a relevant dataset
Perform cleaning, preprocessing, and transformation
Document data decisions for reproducibility
Module 3: Model Design & Training
Select appropriate AI/ML architecture (e.g., CNN, LSTM, DQN)
Train and fine-tune your model
Use validation techniques to avoid overfitting
Module 4: Evaluation & Optimization
Choose performance metrics (accuracy, F1, AUC, etc.)
Perform bias/error analysis
Optimize hyperparameters and retrain if needed
Module 5: Deployment & Presentation
Package the model for deployment (Flask API, Streamlit app, or web interface)
Create a user-friendly demo
Document your solution and prepare a final report
Module 6: Submission & Peer Review
Submit project notebook, report, and live demo link (if applicable)
Review 1–2 peer projects and provide structured feedback
Receive certification upon final evaluation
Tools & Technologies Used:
Python, Scikit-learn, TensorFlow, PyTorch
Jupyter Notebook or Google Colab
Streamlit, Flask, or Gradio (for deployment)
GitHub (for version control and portfolio)
Target Audience:
Learners completing the AI certification track
Developers building real-world AI solutions
Students applying AI to research or startups
Professionals creating AI-powered prototypes
Global Learning Benefits:
Build a complete AI application from scratch
Showcase your expertise in a real-world setting
Strengthen your portfolio for job interviews or research
Demonstrate your ability to solve practical AI challenges end-to-end
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