• Beginners with no prior ML knowledge.

Week 1: Foundations of Machine Learning

Day 1: Introduction to Machine Learning

• What is Artificial Intelligence (AI) and Machine Learning (ML)?
  • AI vs. ML vs. Deep Learning
  • Real-world applications of ML
• Types of ML: Supervised, Unsupervised, and Reinforcement Learning.
• Overview of the ML Workflow: Data, Model, Training, and Evaluation.

 Day 2: Understanding Data and Features

• What is data?
  • Structured vs. Unstructured Data.
• Data Preprocessing: Cleaning and Transforming Data.
• Introduction to Features and Feature Engineering.
• Hands-on: Explore and preprocess a simple dataset using Python (e.g., Iris Dataset).

Day 3: Key Machine Learning Algorithms

• Linear Regression: Predicting numerical outcomes.
•  Decision Trees: Intuitive model for classification and regression.
• K-Means Clustering: Basics of unsupervised learning.
• Hands-on: Build and train a simple regression and classification model using SciKit-Learn.

Day 4: Model Evaluation

• Metrics for evaluating ML models:
  • Accuracy, Precision, Recall, F1 Score (classification).
  • Mean Squared Error (regression).
• Overfitting and Underfitting: Understanding Bias-Variance Tradeoff.
• Hands-on: Evaluate a simple ML model and tune hyperparameters.

Day 5: Introduction to Neural Networks

• Basics of Neural Networks:
  • Perceptrons, Layers, and Activation Functions.
  • Forward Pass and Backpropagation.
• Hands-on: Build and train a simple neural network.

Week 2: Deep Learning Essentials

Day 6: Deep Learning Fundamentals

• What is Deep Learning?
  • Differences between traditional ML and DL.
• Overview of Deep Neural Networks (DNNs).
• Key architectures: Feed-forward Neural Networks (FNNs).
• Hands-on: Build a multi-layer perceptron (MLP) for image classification (MNIST dataset).

Day 7: Convolutional Neural Networks (CNNs)

• Basics of CNNs:
  • Convolutions, Filters, and Pooling.
  • How CNNs excel in image processing tasks.
• Hands-on: Implement a CNN for image classification.

Day 8: Recurrent Neural Networks (RNNs)

• Basics of RNNs:
  • Understanding sequential data.
  • Introduction to Long Short-Term Memory (LSTM) networks.
• Applications: Time series forecasting, text processing.
• Hands-on: Build an RNN for text classification.

Day 9: Optimization and Training Techniques

• Gradient Descent Variants: SGD, Adam, etc.
• Regularization Techniques: Dropout, Batch Normalization.
• Hands-on: Improve CNN or RNN performance using regularization techniques.

Day 10: Introduction to Generative Models

• What is Generative AI?
  • Discriminative vs. Generative Models.
• Overview of Generative Models:
  • Variational Autoencoders (VAEs).
  • Generative Adversarial Networks (GANs).
• Hands-on: Visualize outputs of pre-trained generative models.

Week 3: Generative AI Deep Dive

Day 11: Variational Autoencoders (VAEs)

• Understanding VAEs:
  • Encoder-Decoder Architecture.
  • Latent Space and Sampling.
• Applications: Image generation, anomaly detection.
• Hands-on: Build a VAE for image reconstruction.

Day 12: Generative Adversarial Networks (GANs)

• How GANs work:
  • Generator and Discriminator.
  • Training dynamics and challenges.
• Applications: Image synthesis, style transfer.
• Hands-on: Implement a simple GAN to generate synthetic images.

Day 13: Transformers and Attention Mechanisms

• What are Transformers?
  • Attention Mechanism: Self-Attention and Multi-Head Attention.
  • Overview of GPT and BERT models.
• Applications: Text generation, language modelling.
• Hands-on: Use Hugging Face Transformers for text generation.

Day 14: Text-to-Text Applications

• Fine-tuning Pretrained Models:
  • Text summarization.
  • Question answering.
• Hands-on: Build a text summarizer using Hugging Face.

Day 15: Neural Style Transfer

• Understanding Style Transfer:
  • Content vs. Style Representation.
• Applications in Art and Design.
• Hands-on: Create AI-generated artwork using Neural Style Transfer.

Week 4: Advanced Topics and Applications

Day 16: Music and Video Generation

• Generating Music:
  • RNNs and Pre-trained Models (e.g., Magenta).
• Video Synthesis:
  • GANs for video generation.
  • Ethical concerns around deepfakes.
• Hands-on: Generate music using Magenta.

Day 17: Bias and Ethics in Generative AI

• Ethical Concerns:
  • Bias in AI models.
  • Misinformation through deepfakes.
• Responsible AI Practices.
• Class Discussion: Analyze examples of ethical issues in GenAI.

Day 18: Deployment of Generative AI Models

• Deployment Strategies:
  • Hosting models using Streamlit or Flask.
  • Using cloud platforms like AWS or Google Colab.
• Hands-on: Deploy a generative model with a simple web interface.

Day 19: Capstone Project Planning

• Choose a Project:
  • Text generation, image synthesis, or any real-world GenAI application.
• Plan:
  • Dataset preparation, tools, and expected outcomes.
• Team Collaboration: Work in groups to brainstorm project ideas.

Day 20: Capstone Project Development and Presentation

• Complete and Showcase the Project:
  • Build a working Generative AI application.
• Presentations:
  • Share the project, including challenges and learnings.
  • Peer reviews and feedback.