Introduction to Machine Learning
Last updated: May 15, 2023
What is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a simpler definition: "Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so."
Key Concepts
- Supervised Learning: The algorithm learns from labeled training data.
- Unsupervised Learning: The algorithm learns patterns from unlabeled data.
- Reinforcement Learning: The algorithm learns by trial and error using feedback from its actions.
- Features: The input variables used to make predictions.
- Labels: The output variables we're trying to predict.
Applications of ML
Machine learning is powering many of the services we use today:
Recommendation Systems
Used by Netflix, Amazon, Spotify
Image Recognition
Facebook photo tagging, medical imaging
Natural Language Processing
Chatbots, translation services
Predictive Analytics
Stock market, weather forecasting
Fig 1.1 - Machine learning process flow diagram showing data input, model training, and prediction output.
Fig 1.2 - The three main types of machine learning: supervised, unsupervised, and reinforcement learning.
Concept Map
Interactive concept map showing relationships between key ML concepts.
