Machine Learning (ML) is a branch of AI that enables machines to learn from data and improve their performance without being explicitly programmed. Along with ML, there are other branches in AI that may leverage ML, such as computer vision and natural language processing (NLP).
There are three main types of ML:
1. Supervised Learning: This is used when we have labeled data (e.g., showing the model images labeled "cat" or "dog"). It’s great for tasks where the machine learns from examples with known outcomes. For example, spam detection, image classification, fraud detection, sentiment analysis or house price prediction, etc.
2. Unsupervised Learning: When we don’t have labels, unsupervised learning helps the machine find patterns on its own. This is used in clustering tasks like customer segmentation (can be used to recommend products or movies that “people like you have enjoyed”), anomaly detection, market basket analysis (understand which products are often purchased together), etc.
3. Reinforcement Learning: Here, machines learn through trial and error, receiving feedback on their actions. It’s ideal for situations where the machine needs to continuously improve by learning from successes and mistakes. Use cases include game AIs, self-driving vehicles, personalized recommendations, robotics, dynamic pricing, investment portfolio management, supply chain optimization, energy management, etc.
