How it works
- Uses algorithms: Machine learning relies on algorithms that are trained on data to find patterns and build models.
- Learns from data: The more data the system processes, the better its performance becomes over time.
- Makes predictions/decisions: The models can then use their learned patterns to classify data (e.g., spam vs. not spam) or predict future outcomes (e.g., a customer’s next purchase).
Types of machine learning
- Supervised learning: Uses labeled data to train models for tasks like classification or regression (predicting numerical values).
- Unsupervised learning: Finds patterns in unlabeled data, such as clustering similar data points together or identifying anomalies.
- Reinforcement learning: Involves an agent learning to make a sequence of decisions in an environment to maximize a reward.
- Generative AI: Creates new, original content like text, images, or music by learning from existing data and mimicking its patterns.
This video explains the different types of machine learning:
1m
Real-world applications
- Personalized recommendations: Suggesting products, movies, or music based on past user behavior.
- Fraud detection: Identifying suspicious credit card transactions or login attempts by learning normal spending or activity patterns.
- Self-driving cars: Powering the decision-making systems that allow vehicles to navigate.
- Medical diagnostics: Analyzing medical images to help identify potential illnesses or risks.
- Chatbots: Providing automated customer support by learning from past conversations.
This video explains how machine learning is used in real-world applications