Introduction to Artificial Intelligence (AI)

Welcome to the Introduction to Artificial Intelligence course! In this section, you will learn the basics of AI and how it is transforming various industries.

Course Outline

• Module 1: Understanding AI - An overview of what AI is, its history, and its applications.
• Module 2: Machine Learning - Introduction to machine learning algorithms and their types.
• Module 3: Deep Learning - The fundamentals of deep learning and neural networks.
• Module 4: AI Ethics - Discussing the ethical implications of AI and its impact on society.
• Module 5: AI in Practice - Real-world examples of AI applications across different fields.

Learning Resources

• Introduction to Python for AI
• AI in Healthcare

Key Concepts

• Machine Learning: A subset of AI that involves the development of algorithms that can learn from and make predictions or decisions based on data.

  • Supervised Learning: Learning from labeled data.
  • Unsupervised Learning: Learning from unlabeled data.
  • Reinforcement Learning: Learning from interactions with an environment.

• Deep Learning: A subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.

  • Neural Networks: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

• Natural Language Processing (NLP): The ability of computers to understand, interpret, and generate human language.

Practice

To deepen your understanding, try out the following exercises:

• Build Your First AI Model

Artificial Intelligence