Course Syllabus
AI Syllabus (Beginner to Intermediate)
Here is a simple, structured AI syllabus you can follow for study or teaching.
Module 1: Introduction to AI
- What is Artificial Intelligence?
- Types of AI: ANI, AGI, ASI
- Weak AI vs Strong AI
- Applications of AI in real world
- History and evolution of AI
Module 2: Mathematics for AI
- Linear Algebra: vectors, matrices
- Probability & Statistics
- Calculus basics (derivatives, gradients)
- Optimization concepts
Module 3: Programming for AI
- Python basics
- Libraries: NumPy, Pandas, Matplotlib
- Introduction to Jupyter Notebook
Module 4: Machine Learning (ML)
- What is ML?
- Supervised vs Unsupervised Learning
- Regression
- Classification
- Clustering
- Model evaluation (accuracy, precision, recall)
- Overfitting and underfitting
Module 5: Deep Learning (DL)
- Neural Networks
- Activation functions
- Backpropagation
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Popular frameworks: TensorFlow / PyTorch
Module 6: Natural Language Processing (NLP)
- Text preprocessing
- Sentiment analysis
- Word embeddings
- Transformers
- Large Language Models (LLMs)
Module 7: AI Ethics & Safety
- Bias in AI
- Privacy and security
- Responsible AI practices
- Future of AI
Module 8: Projects
Choose any:
- Movie recommendation system
- Chatbot
- Image classifier
- Stock price prediction
- Text summarizer
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