Machine Learning

What is Machine Learning?

Machine learning (ML) is a subfield of Artificial Intelligence for the studying of algorithms that computer systems can use to derive knowledge from data.

Machine learning algorithms are used to build mathematical models of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.

There are three different types of machine learning algorithms:

1. Supervised Learning. The data is labeled with the expected outcome in a “training dataset” that will help the system train itself to predict the outcome on new (previously unseen) data samples.
2. Unsupervised Learning. Here the machine has no inputs in terms of expected outcomes and labels but it simply gets the features as numerical attributes and will find the hidden structure of the dataset.
3. Reinforcement Learning. It helps with decision-making tasks. The system gets a reward when it is capable of making measurable progress on a given action without knowing how to get to the end. A typical example is the chess game. The system learns by evaluating the results of a single action (i.e. moving of one square the horse).

Machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In this blog, we focus on the use of machine learning for search engine optimization, natural language processing, knowledge graph and structured data.

Jeff Dean, Senior Fellow, and SVP at Google announced the introduction of Pathways: a new AI architecture multi-tasking, multi-modal, and more efficient.

To find out more about Google Pathways, check out our Web Stories here.