Course Contents
Day 1: Understanding Artificial Intelligence
- Introduction to AI
- Definition and brief history of AI
- Importance and applications of AI in various fields
- Types of AI
- Narrow AI vs. General AI
- Examples of AI applications in real life
- Machine Learning Basics
- What is Machine Learning (ML)?
- Supervised, Unsupervised, and Reinforcement Learning
- Basic algorithms: Linear Regression, Logistic Regression, and Decision Trees
- Deep Learning
- Introduction to Neural Networks
- Structure of a neural network: neurons, layers, and activation functions
- Popular deep learning architectures: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Day 2: Practical Applications and Ethical Considerations
- AI in Practice
- Real-world applications of AI: Image recognition, Natural Language Processing (NLP), Autonomous vehicles, etc.
- Case studies and examples of successful AI implementations
- Data for AI
- Importance of data in AI applications
- Data preprocessing: cleaning, normalization, and feature engineering
- Introduction to Tools and Frameworks
- Popular AI frameworks: TensorFlow, PyTorch, and scikit-learn
- Introduction to Python programming language for AI development
- Ethical Considerations in AI
- Bias and fairness in AI algorithms
- Privacy and security concerns
- Responsible AI development and deployment practices
- Future of AI
- Emerging trends and advancements in AI technology
- Potential impacts of AI on society and the workforce
