Lesson 1: Overview of AI
Lesson 2: History and Evolution
Lesson 3: Types of AI
Lesson 4: Applications of AI

Lesson 1: Introduction to Machine Learning
Lesson 2: Supervised Learning
Lesson 3: Unsupervised Learning
Lesson 4: Regression
Lesson 5: Classification
Lesson 6: Clustering
Lesson 7: Dimensionality Reduction

Lesson 1: Introduction to Neural Networks
Lesson 2: Perceptron
Lesson 3: Multi-layer Perceptron (MLP)
Lesson 4: Backpropagation
Lesson 5: Convolutional Neural Networks (CNNs)
Lesson 6: Recurrent Neural Networks (RNNs)
Lesson 7: Long Short-Term Memory (LSTM)
Lesson 8: Autoencoders
Lesson 9: Generative Adversarial Networks (GANs)

Lesson 1: Introduction to NLP
Lesson 2: Tokenization
Lesson 3: Part-of-Speech Tagging
Lesson 4: Named Entity Recognition (NER)
Lesson 5: Sentiment Analysis
Lesson 6: Text Classification
Lesson 7: Language Modeling
Lesson 8: Word Embeddings (e.g., Word2Vec, GloVe)

Lesson 1: Introduction to Computer Vision
Lesson 2: Image Processing Basics
Lesson 3: Image Classification
Lesson 4: Object Detection
Lesson 5: Image Segmentation
Lesson 6: Feature Extraction
Lesson 7: Convolutional Neural Networks for Vision

Lesson 1: Ethical Considerations in AI
Lesson 2: Bias in AI
Lesson 3: Fairness, Accountability, and Transparency (FAT) in AI
Lesson 4: Bias Mitigation Techniques

Lesson 1: AI in Healthcare
Lesson 2: AI in Finance
Lesson 3: AI in Marketing
Lesson 4: AI in Robotics AI in Autonomous Systems
Lesson 5: Case Studies and Practical Applications