Course Information
- Duration4 weeks (3 hours per week)
Description
Step into the world of Artificial Intelligence with our comprehensive AI course designed for beginners and intermediate learners alike. Whether you're just starting your journey or looking to deepen your knowledge, this course provides a strong foundation in AI concepts, tools, and practical applications.
You'll explore everything from the fundamentals of AI and Machine Learning to cutting-edge technologies like Deep Learning, Computer Vision, and Natural Language Processing. With a balance of theory and hands-on practice, you'll build real AI projects, explore ethical implications, and gain insight into career opportunities in this fast-evolving field.
Course Outline
Module 1: Introduction to AI
- What is Artificial Intelligence?
- Definition of AI
- History and evolution of AI
- Real-world applications of AI (e.g., healthcare, automotive, finance)
- Types of AI
- Narrow AI
- General AI
- Superintelligent AI
- AI vs. Machine Learning vs. Deep Learning
- Key differences and overlaps
- Examples and use cases
Module 2: Fundamental Concepts in AI
- Understanding Data
- Types of data (structured, unstructured)
- Data collection and preparation
- Algorithms and Models
- Overview of key algorithms (classification, regression, clustering)
- Model training and testing basics
- AI Ethics and Bias
- Importance of fairness in AI
- Social and ethical implications of AI
Module 3: Machine Learning Basics
- What is Machine Learning?
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Steps in a Machine Learning Project
- Data preprocessing
- Model building
- Evaluation and optimization
- Common Tools and Libraries
- Scikit-learn
- TensorFlow
- PyTorch
Module 4: Deep Learning Overview
- Introduction to Neural Networks
- What is a neural network?
- Structure of a neural network (input, hidden, output layers)
- Deep Learning Frameworks
- Basic Applications of Deep Learning
- Image recognition
- Natural Language Processing (NLP)
- Speech-to-text systems
Module 5: Practical Applications of AI
- AI in Everyday Life
- AI in virtual assistants (Alexa, Siri)
- AI in recommendations (Netflix, Spotify)
- Building a Simple AI Project
- Example: Predicting house prices
- Tools: Google Colab, Jupyter Notebooks
- AI for Automation
- Introduction to robotics
- AI in smart homes and IoT
Module 6: Future of AI
- Trends in AI
- Generative AI (e.g., ChatGPT, DALL·E)
- Autonomous systems (self-driving cars, drones)
- Opportunities in AI Careers
- AI-related job roles
- Skills required to become an AI professional
- Challenges and Limitations of AI
- Current limitations in AI technology
- Addressing ethical dilemmas
Module 7: Hands-on Workshops (Optional)
- Build a Chatbot
- Using Python and libraries like Rasa or NLTK
- Image Classification Model
- Sentiment Analysis Tool
- Using Natural Language Processing
Module 8: Final Assessment and Resources
- Capstone Project
- Build an AI solution to a real-world problem
- Certification and Next Steps
- Resources for advanced learning
- Joining AI communities and forums