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What is Machine Learning and How Does It Work?

Learn what machine learning is, how it differs from AI, and why it’s so important.

By Sasha McGregorPublished 3 years ago 3 min read

The world of artificial intelligence is vast, contentious, and sometimes confusing, so it’s easy to find the terminology dense, particularly when words like “algorithm,” “machine learning,” and “neural net” are often used interchangeably by marketing departments trying to keep up with the changing world of smart computing. Of these terms, machine learning is perhaps most frequently conflated with AI, but they're not the same thing. Keep reading to learn what machine learning is, how it differs from AI, and why it’s so important.

What Does Machine Learning Really Mean?

Machine learning is a specific branch of artificial intelligence that’s perfectly poised to take advantage of the wide availability of big data. Tom M. Mitchell, the former Chair of the Machine Learning Department at Carnegie Mellon University, described the field as “the study of computer algorithms that allow computer programs to automatically improve through experience.” In practice, this is a type of intelligence in which machines learn like humans, collecting information and using it to adjust their understanding of various problems. Having sufficient high-quality data that fits the model’s purpose is so essential to machine learning architecture that it’s always the first step in designing a machine learning system; just as a human student might struggle to understand a completely new topic, machines can’t learn without having the information they need.

What Type of Data Do Machines Need?

Data ingestion is essential to machine learning, but the type of data, the way data scientists provide it and how they expect the model to use it are all important variables that affect the final results of machine learning processes. There are several different applications of machine learning, including natural language processing, which analyzes spoken or written human languages to flag certain words and parse broader meaning; computer vision, which classifies and interprets still images or video; and statistical analysis, which uses numerical data sets to identify trends and forecast outcomes.

Each of these processes requires a different type of input data to “train” the models, whether pictures, words, or numbers, and since most applications use several different types of data, ensuring that disparate data types are all processed appropriately is a key component of building a successful model. Further, the type of machine learning model in use informs the structure of the dataset that humans need to provide for it. If the model is responsible for looking for a predetermined pattern in a well-organized dataset, data scientists should take a supervised learning approach; conversely, if the goal is to discover commonalities in an undifferentiated dataset, researchers will need to design an unsupervised model.

How Can Humans Understand Machine Learning Insights?

The unique insights that machine learning can provide are the real reason for the excitement surrounding it, but understanding computer outputs can be notoriously difficult, so the last step in building a successful machine learning application is integrating external systems for output. In both business and academic settings, data scientists will need to accommodate requests for information from a variety of users and devices, so the best solution is usually to build an interface described as a RESTful API.

One familiar example of an API, short for Application Programming Interface, is a smartphone weather app; the app sends a query to the database that contains current weather information and displays it on the phone. RESTful APIs are those that use REST, or REpresentational State Transfer, architecture. It’s a good tool for meeting the needs of diverse groups of users and devices because each client query stands alone, so information about querying devices doesn’t have to stay on the server; further, it separates the end user interface from the database server itself, which keeps the server simple, supporting stability and scalability. Output from the model can even be used for further machine learning applications.

The Bottom Line

The details of machine learning are complex, but the process—collecting data, recognizing patterns, and delivering insights—is relatively simple. While machine learning isn’t the only useful type of AI, it’s one of the most promising branches of the technology today, and opportunities for its application in research, business and education will undoubtedly continue to expand.


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