Machine learning is a branch of computer science and artificial intelligence that automates analytical model building. It is based on the belief that systems can learn from data, identify patterns and make decisions with minimal human intervention. As models are exposed to new data, they are able to independently adapt, learning from previous computations and creating reliable and repeatable results.

Machine learning is not a new discipline. Companies like Google, Netflix and Facebook all use machine learning models to innovate. Web developers, scientists and data scientists may all use machine learning.

Machine learning courses, both online and in person, can help everyone from beginners to advanced users further their careers. Learn more about the best machine learning online courses.

Machine learning courses offer everything from the fundamentals of machine learning to advanced practices and statistical modeling. There are machine learning online courses for most people who are interested, from beginners to experts. Many machine learning courses are available online for free, meaning there’s little barrier to learning more about this valuable field.

Below are some machine learning courses available online. Machine learning fundamentals courses offer an introduction to machine learning and its basic principles, such as data mining and statistical pattern recognition. Courses will likely include theoretical and practical instruction, so you can learn some basics of implementing machine learning techniques.

While machine learning is often used by data scientists, it can also help many professionals and businesses. Courses for machine learning in business provide an understanding the advantages, limitations and scope of machine learning from a management perspective. Machine learning can be used in business to streamline decision-making, analyze data and reduce errors when processing large amounts of data.

Deep learning courses are advanced and teach students how to run supervised, semi-supervised and unsupervised learning.

Much like how humans process information and then communicate through a biological neural network, deep learning algorithms can process huge amounts of data, then take those datasets and find essential meanings. Deep learning allows the user to develop broader and more in-depth solutions, as opposed to task-specific solutions like in traditional machine learning.

The terms machine learning, artificial intelligence and deep learning are often thrown around interchangeably, but they are all distinct disciplines and techniques. Artificial intelligence is the science of training machines to perform human tasks, whereas machine learning is a subset of artificial intelligence that instructs a machine how to learn.

Artificial intelligence is the broadest term to describe advanced computer intelligence. According to the Brookings Institution, AI is thought to refer to machines that “respond to stimulation consistent with traditional responses” like humans, while taking into account humans’ ability for contemplation, judgment and intention.” An example would be IBM’s Deep Blue, which beat chess grandmaster Garry Kasparov at the game in 1996. Deep Blue relied on rule-based artificial intelligence: If a player did X, the next move should be Y.

Machine learning is a subfield of artificial intelligence. According to O’reilly, the core principle is that machines take data and “learn” for themselves. Google DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, extrapolated the strategy of the game by studying a large dataset of expert moves. This was a form of machine learning.

Machine learning is useful for businesses because it reduces work and room for error. There are a number of careers that deal with machine learning, including:

Many businesses can benefit from the analysis and interpretation of large amounts of data. Machine learning may provide valuable insights in your current business or open new career paths at other organizations.

Last updated: July 2020