Week 1: Foundations
- Introduction to AI, ML, DL.
- Real-world applications (chatbots, recommendation systems, healthcare AI).
- Hands-on: Setting up Python environment (Anaconda/Jupyter).
Week 2: Data & Machine Learning Basics
- Understanding datasets, features, labels.
- Supervised vs. unsupervised learning.
- Hands-on: Build a simple regression model in Python.
Week 3: Classification & Clustering
- Decision trees, k-means clustering.
- Case study: Spam email detection.
- Hands-on: Classification project with Scikit-learn.
Week 4: Neural Networks
- Basics of neurons, layers, activation functions.
- Introduction to TensorFlow/Keras.
- Hands-on: Build a simple neural network for image recognition.
Week 5: AI Tools & Platforms
- Python libraries: NumPy, Pandas, Matplotlib.
- Cloud AI services: Oracle Cloud AI, Google AI, Azure Cognitive Services.
- Hands-on: Using cloud APIs for sentiment analysis.
Week 6: Ethics & Responsible AI
- Bias in AI systems.
- Privacy and data security.
- Group discussion: “Should AI replace human jobs?”
Week 7: Capstone Project Development
- Students choose a project (chatbot, image classifier, recommendation system).
- Guided mentoring sessions.
Week 8: Project Presentation & Feedback
- Students present their projects.
- Peer review + instructor feedback.
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