- What is Machine Learning? Types and Applications
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Evaluation Metrics for Machine Learning Models
- Introduction to Scikit-Learn for Machine Learning in Python

- Introduction to Neural Networks
- Activation Functions and Feedforward Neural Networks
- Backpropagation Algorithm for Training Neural Networks
- Introduction to TensorFlow or PyTorch for Deep Learning
- Building and Training Simple Neural Networks for Regression and Classification Tasks

- Introduction to Convolutional Neural Networks
- CNN Architecture: Convolutional Layers, Pooling Layers, Fully Connected Layers
- Training CNNs for Image Classification Tasks
- Transfer Learning and Fine-Tuning Pretrained CNN Models
- Hands-on Project: Image Classification with CNNs

- Introduction to Recurrent Neural Networks
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
- Applications of RNNs in Natural Language Processing (NLP)
- Text Preprocessing Techniques: Tokenization, Padding, Word Embeddings
- Building Sequence Models for Text Generation and Sentiment Analysis

- Introduction to Generative Adversarial Networks (GANs)
- Autoencoders and Variational Autoencoders (VAEs)
- Deep Reinforcement Learning
- Hyperparameter Tuning and Optimization Techniques
- Ethics and Bias in AI and Machine Learning

- Model Serialization and Saving
- Model Deployment Options: Flask API, Docker, Cloud Services
- Introduction to Model Serving Frameworks like TensorFlow Serving
- Integrating Machine Learning Models with Web Applications
- Monitoring and Scaling Machine Learning Models in Production

- Capstone Project: Design and implement a real-world machine learning application from scratch
- Presentations and Peer Review
- Advanced Topics: Cutting-edge research papers, Industry Trends, and Future Directions in AI and Machine Learning