What is Machine Learning (ML)?
Gives computers the ability to learn from data and improve over time without being explicitly programmed for every single task
Example: A spam filter that gets better at catching new types of spam emails as it sees more examples.
What is Deep Learning (DL)?
A subfield of ML that uses ‘deep’ neural networks to learn complex patterns
Example: Image recognition where the AI can tell a picture of a cat from a picture of a dog by analyzing millions of photos.
What does Natural Language Processing (NLP) focus on?
Enabling machines to understand, interpret, and generate human language
Example: The technology behind Siri or Alexa that understands your voice commands and responds appropriately.
What is the purpose of Computer Vision (CV)?
Helps machines ‘see’ and interpret visual information from the world, like images and videos
Example: A self-driving car identifying other cars, pedestrians, and traffic lights on the road in real time.
What is Reinforcement Learning (RL)?
An AI learns by trial and error, receiving ‘rewards’ for good decisions and ‘penalties’ for bad ones
Example: An AI agent learning how to play a game of chess or Pac-Man and eventually becoming a super-human player.
True or false: The key is to use AI as a tool to enhance your applications and solve real problems, not just as a gimmick.
TRUE
Experimenting with simple projects on platforms like Google’s Teachable Machine or Scratch can be a great way to start building your skills.
