Where is machine learning used?

Machine Learning algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

____________ learning trains algorithms based on example input and output data that is labeled by humans?

Supervised learning uses patterns to predict label values on additional unlabeled data?

Type of Supervised learning is?

Classification: A classification problem is when the output variable is a real value, such as “dollars” or “weight”.

Regression: A regression problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.

___________ learning which provides the algorithm with no labeled data in order to allow it to find structure within its input data.

The goal of unsupervised learning is discovering hidden patterns within a dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data?

Unsupervised learning is commonly used for transactional data?

Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next?

Types of Unsupervised learning are?

Clustering: An clustering problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Association: A association rule problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

A ____________ algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly.

In reinforcement learning, the agent learns without intervention from a human by maximizing its reward and minimizing its penalty. It is a type of dynamic programming that trains algorithms using a system of reward and punishment?

___________ learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. It falls between unsupervised learning and supervised learning.