Types of Machine Learning - Unsupervised Learning

Types of Machine Learning - Unsupervised Learning 🎯

While Supervised Learning relies on labeled data, Unsupervised Learning takes a different approach. Here, the algorithm is provided with data that doesn’t have explicit labels or categories. Instead, the model aims to find hidden patterns or structures within the data.

🔍 How It Works:

Unsupervised Learning is often used for clustering, where the algorithm groups similar data points together based on their features. For example, a retailer might use Unsupervised Learning to segment customers into different groups based on their purchasing behavior, even if those segments aren’t predefined.

🔍 Real-World Applications:

This approach is particularly useful in exploratory data analysis and customer segmentation. For instance, it can help businesses discover new customer segments that weren’t previously known, allowing for more targeted marketing strategies. It’s also used in anomaly detection, such as identifying fraudulent transactions by recognizing patterns that deviate from the norm.

Unsupervised Learning is powerful for discovering hidden insights, but there’s more to explore. In the next post, we’ll delve into Reinforcement Learning, a dynamic and interactive type of ML.