Unsupervised Learning

The video introduces three types of machine learning: supervised, unsupervised, and reinforcement learning, focusing on unsupervised learning. It explains that unsupervised learning deals with unlabeled data, allowing models to discover hidden patterns without supervision. An example involving a baby recognizing pets illustrates this concept. The video highlights the benefits of unsupervised learning, such as finding unknown patterns and easier data acquisition. It also covers two main types of unsupervised learning: clustering, which identifies patterns in data, and association, which finds relationships between variables. The video concludes with a preview of the next topic, K-means clustering.

What is a key characteristic of unsupervised learning?

• It is always supervised by a human.

• It uses unlabeled data.

• It involves reinforcement signals.

• It requires labeled data.

Why might unsupervised learning be preferred over supervised learning?

• It is faster to train models.

• It can find unknown patterns without labeled data.

• It requires less data.

• It is more accurate.

Which of the following is a type of unsupervised learning?

• Regression

• Clustering

• Classification

• Reinforcement

What is the main goal of clustering in unsupervised learning?

• To label data points

• To find patterns or similarities in data

• To predict future outcomes

• To establish causal relationships

Which algorithm is commonly used for association in unsupervised learning?

• Apriori

• Decision Trees

• Linear Regression

• K-means