Course Syllabus
Full AI Course Syllabus (Beginner to Advanced)
You can use this for a 10–14 week course or modify it as needed.
Module 1: Introduction to AI
Topics
- What is Artificial Intelligence?
- History and evolution of AI
- Types of AI: Narrow, General, Super AI
- Real-world applications (healthcare, finance, robotics)
Learning Outcomes
- Understand basic AI concepts & terminology
- Identify where AI is used in real life
Module 2: Mathematics for AI
Topics
- Linear algebra (vectors, matrices)
- Probability & statistics basics
- Calculus (derivatives, gradients)
Learning Outcomes
- Gain foundational math skills required for ML algorithms
Module 3: Programming for AI
Topics
- Python basics
- Libraries: NumPy, Pandas, Matplotlib
Hands-On
- Data structures
- Reading/writing datasets
- Simple data visualizations
Learning Outcomes
- Write basic Python programs
- Manipulate and visualize data
Module 4: Machine Learning Fundamentals
Topics
- What is Machine Learning?
- Supervised, Unsupervised & Reinforcement Learning
- Regression, Classification, Clustering
Algorithms Covered
- Linear Regression
- Logistic Regression
- K-Means
- Decision Trees
- Naive Bayes
Hands-On
- Build simple ML models using Scikit-Learn
Learning Outcomes
- Understand ML workflow
- Train & evaluate ML models
Module 5: Deep Learning
Topics
- Neural Networks basics
- Activation functions
- Backpropagation
- Loss functions
Libraries
Hands-On
Learning Outcomes
- Understand how neural networks work
Module 6: Computer Vision
Topics
- Image processing basics
- Convolutional Neural Networks (CNNs)
- Transfer learning
Hands-On
- Build an image classifier (e.g., cat vs. dog)
Learning Outcomes
- Process images and classify them using CNNs
Module 7: Natural Language Processing (NLP)
Topics
- Text preprocessing
- Tokenization, stemming, lemmatization
- Sentiment analysis
- Transformer models (BERT, GPT)
Hands-On
- Build a text classification model
Learning Outcomes
- Understand how machines read and understand text
Module 8: Generative AI
Topics
- Large Language Models (LLMs)
- Diffusion models
- Prompt engineering
- ChatGPT, DALL·E and other AI tools
Hands-On
- Create text, images, and audio using GenAI tools
Learning Outcomes
- Use and understand generative AI systems
Module 9: AI Ethics & Safety
Topics
- Bias in AI
- Responsible AI
- Data privacy
- Regulations
Learning Outcomes
- Apply ethical principles when building AI systems
Module 10: AI Project Development
Topics
- Dataset preparation
- Model lifecycle (training, evaluation, deployment)
- MLOps basics
Final Project Examples
- Chatbot
- Image classifier
- Recommendation system
- Fraud detection model
Learning Outcomes
- Build a complete end-to-end AI project
📜 Deliverables (Optional)
- Assignments after each module
- Mid-term quiz
- Final exam
- Capstone project presentation
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