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Types of Machine Learning

Businesses everywhere are turning to AI and machine learning to unleash the power of their data. Machine learning algorithms have become an integral component of contemporary data analysis.

Google, Twitter and Facebook all use machine learning for things like targeted advertising to prioritizing news feeds to facial recognition. ML is not only for billion dollar companies, though. Thousands of relatively anonymous corporations level machine learning for insights and improved efficiency. They use a few different types of machine learning to achieve their goals.


Supervised learning is the most specific training methodology. A supervised learning algorithm is informed as to the relationships between inputs and outputs. The outputs exist within finite universe.

Supervised learning requires a significant amount of labeling before running the algorithm. While this method allows for greater control over the data and the classes within the data, it does limit the potential of ML.

With supervised learning, the algorithm will not discover unknown insights the way that unsupervised learning can.

It can also be labor intensive and have a limited upside. Supervised learning enables great specificity regarding the definition of classes, but it can become unruly when working with big data.

Other types of machine learning are worth looking into before undertaking such an endeavor.


Unsupervised learning reveals some of the mystique and allure behind machine learning. With these algorithms, a machine finds patterns on its own without much guidance at all.

The machine forms classifications without data first being labeled. There is greater strength in the algorithm, which drives decisions.

Unsupervised learning is beneficial when there's little initial insight into the data. It can bring structure to an existing data set in which the variables are unclear to the human eye.

With a combination of AI and data, the algorithm infers patterns on its own. More experimenting is needed, and that can demand more of a hands-on approach after the fact. Additionally, without labels, the machine's goals and objectives are unknown.

Through clustering large amounts of data, unsupervised learning algorithms can reveal otherwise unavailable insights.


Existing in the space between the aforementioned areas, semi-supervised learning combines features of both supervised and unsupervised learning.

Within this framework, the algorithm is trained with some rules (but not all like in supervised learning). The machine learns from experience and can apply rules to unclassified data.


Through adapting to environment, reinforcement learning algorithms optimize behavior within a defined data set.

Reinforcement learning operates without a training data set and learns only through its own experience.

This is the methodology being many familiar products and features, including search engine result refining, virtual assistants, online product recommendations, and online fraud detection.

These models are trained through human reinforcement, both positive and negative. Reinforcement learning is ideal when the best -- or only -- way to gather knowledge about the environment is to interact with it.


In these four types of machine learning, every algorithm is trained differently. One key to success is understanding the approach that best fits your data, your needs and your budget.

Learning data modeling will improve your comfort level with machine learning.

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