Introduction to Artificial Intelligence (AI)

Artificial intelligence (AI) refers to the computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems. 

It involves creating machines that can think like humans and imitate their actions.

Types of AI

1. Narrow AI (Weak AI):

   • Designed to perform a single task.
   • Example: Virtual assistants like Siri or Alexa.

2. General AI (Strong AI):

   • Has human-like cognitive abilities across multiple domains (still theoretical).
   • Example: A machine capable of understanding, reasoning, and learning any task a human can do.

3. Super AI:

   • Surpasses human intelligence (currently a concept).

Key concepts:

1. Machine Learning: focuses on developing systems that learn or improve performance, based on the data. Example: Predicting weather or stock prices.

2. Deep Learning: A subset of ML using neural networks to mimic the human brain. Example: Image and speech recognition.

3. Neural Networks: Enables machines to understand and generate human language. Example: Chatbots and language translators.

4. Datasets: Datasets are large collections of information that AI systems use to learn.

5. LLM

6. NLP

7. Computer Vision: Focuses on enabling machines to interpret visual data. Example: Facial recognition systems.

Machine Learning:

1. Supervised Learning: is a learning process where algorithm learns from labeled data.

Tools for Supervised Learning:

Python Libraries:

• scikit-learn.
• TensorFlow.
• PyTorch.

Platforms:

• Azure ML, AWS SageMaker, Google AI.

2. Unsupervised Learning 

3. Reinforcement Learning