Artificial Intelligence
Artificial Intelligence (AI) is a transformative field that has the potential to revolutionize industries and shape the future of technology. This course, “Introduction to Artificial Intelligence,” offers an engaging exploration of the core principles, techniques, and applications of AI. Whether you’re a curious beginner or a professional looking to enhance your AI knowledge, this course provides a solid foundation in the exciting world of AI.
Course Objectives:
By the end of this course, students will:
Understand the Fundamentals of AI: Develop a clear understanding of what artificial intelligence is, its historical context, and its significance in today’s technological landscape.
Problem-Solving with AI: Learn how AI can be used to solve complex problems and make informed decisions in various domains.
Machine Learning Essentials: Explore the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.
Deep Learning: Dive into deep learning, neural networks, and understand how they power modern AI applications such as image recognition and natural language processing.
AI Ethics and Bias: Examine the ethical considerations surrounding AI, including issues related to fairness, bias, and transparency in AI algorithms.
AI Applications: Explore real-world applications of AI in industries such as healthcare, finance, autonomous vehicles, and robotics.
Hands-On AI Projects: Gain practical experience by working on AI projects and coding exercises that reinforce the concepts learned in the course.
Future Trends in AI: Discover emerging trends in AI, including reinforcement learning, generative adversarial networks (GANs), and AI in the context of the Internet of Things (IoT).
- Understanding Artificial Intelligence
- History and Evolution of AI
- AI vs. Machine Learning vs. Deep Learning
- AI Applications in Various Industries
- Ethics and Responsible AI
- Setting Up Your AI Development Environment
Introduction to Machine Learning
Supervised Learning Algorithms (Regression, Classification)
Model Evaluation and Metrics
- Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)
- Feature Engineering and Selection
- Model Deployment Basics
- Introduction to Deep Learning
- Building Neural Networks with TensorFlow and Keras
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer Learning and FineTuning
- Computer Vision Applications
Basics of Natural Language Processing
Text Preprocessing and Tokenization
NLP Models (RNNs, LSTMs, Transformers)
- Named Entity Recognition (NER)
- Sentiment Analysis
- Language Generation
- Introduction to Reinforcement Learning
- Markov Decision Processes (MDPs)
- QLearning and Policy Gradient Methods
- Reinforcement Learning in Practice
- AI Ethics and Bias Mitigation
- AI in Healthcare, Finance, and Autonomous Systems
Capstone AI Project: Develop & present your own application.
Special Topics (Choose from topics like generative adversarial networks (GANs), AI in robotics, AI research trends, or any emerging AI technologies)
