Final Capstone Project: Designing and Deploying a Complete AI System

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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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