What do your spam folder and a self-driving car have in common? While it may seem counter-intuitive, both employ a branch of artificial intelligence called machine learning, which is widely used throughout the world of data science. Machine learning employs the use of complex data (including big data) and algorithms to replicate how humans learn. Essentially, machine learning mechanisms are designed to improve over time, much like human beings do. While this can include several mundane technologies, such as providing recommendations for streaming services, it is also the backbone of many growing technologies.

The field of data science heavily employs machine technologies, and is increasingly relying upon this form of artificial intelligence to build out its applications, as data science algorithms use various forms of statistics to gain key insights from data. Ultimately, this data is then transformed into actionable insights by the many companies that use them, typically improving the customer experience while also bringing increased productivity or profitability. Big data is only going to become larger and more complex, so machine learning is expected to grow as an industry in the years to come.

Types of Machine Learning

There are four noted approaches to machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each is categorized by the kind of algorithms it employs, and all of them are best suited to different situations. Here’s what they each entail:

Supervised Learning

When using supervised learning, data scientists supply an algorithm that has clearly labeled “training” data.” They then define assorted variables that they want their algorithm to check for any correlations. For example, a program might be fed images of animals until it can accurately identify a variety of such creatures.

Unsupervised Learning

Unsupervised learning, on the other hand, employs algorithms that do not directly define or label their data. The algorithm instead absorbs the data and tries to make meaningful connections between each data point. Interestingly, this can often find connections that humans don’t naturally try to make, giving insights into data that we might otherwise ignore when trying to parse through it ourselves.

Semi-Supervised Learning

Semi-supervised learning uses a mixture of training data and freeform data interpretation. The algorithm will be fed some training data, ensuring that it’s working within an understood sphere of reference, but it’s also allowed to make connections outside of that sphere. The results of this can be fascinating, but it is the most complex of the algorithms and has more potential fail points.

Reinforcement Learning

This form of learning employs the use of positive and negative feedback to guide the algorithm in a specific direction. A data scientist might employ this form of learning in order to teach a machine a complex process with clearly defined rules. The algorithm itself makes decisions, but the data scientist guides and reinforces the results until desired results occur.

What Kind of Algorithms Are Used in Machine Learning?

Machine learning processes can be better understood by examining the kinds of algorithms that are regularly employed. While proprietary algorithms are increasingly seen across the industry, the following are the most common algorithms used:

Neural Networks

This kind of algorithm aims to replicate how the human brain works, using linked processing nodes. Neural networks specialize in pattern recognition, making them ideal for translating text and speech, recognizing images, and creating images of their own.

Linear Regression

This kind of algorithm specializes in predicting how numbers will work based on linear relationships. It is regularly used to understand various economic elements, like predicting inflation.

Decisions Trees

Decision trees employ a branching set of linked decisions. This type of algorithm is typically used to predict numbers while also helping to classify and define data within key categories.

Logistical Regression

This kind of algorithm predicts results based on specific categories. Your spam folder is set up to filter out any emails that fail to meet the criteria that this kind of algorithm checks for.

Clustering

Clustering algorithms are designed to find patterns in data that can then easily be grouped or clustered together. This helps ensure that key elements of data are always placed in the correct categories and consistently tracked.

Notable Machine Learning Examples

Here are some examples of machine learning that you might be familiar with already:

Speech and Text Recognition

Using natural language processing, this kind of machine learning can sift through speech or text data to translate it. A lot of mobile devices take advantage of this to translate speech to text and vice versa.

Customer Service

Many common issues that customers come across don’t actually require direct interaction with a customer service agent. Instead, the algorithm can identify what common concerns a person is dealing with, such as a shipping or payment error, and can guide the user to a solution without needing a live person.

AI Visions

Some kinds of artificial intelligence can learn from visual elements, automatically tagging or categorizing key visual elements. This can assist in the identification of medical abnormalities, find important elements in social media postings, and otherwise help computers better understand visuals as a whole.

Algorithmic Recommendations

This is one of the most common forms of machine learning employed today, and there are many different kinds of algorithms that identify patterns in user behaviors. This then provides recommendations for almost any industry, including retail product recommendations, entertainment recommendations, and more.

Machine Learning Today

As we’ve already seen, machine learning is increasingly being used across many different industries. All of the most popular applications and social media platforms use machine algorithm-driven machine learning processes to better serve their users and bring greater profits. The following are other examples of machine learning processes that can be found in everyday interactions:

Autonomous Vehicles

While self-driving cars used to only exist in the realm of sci-fi novels, pattern recognition algorithms are making these an increasingly safe option for future travel.

Automated Customer Management

Customer service can be simplified by sending customers to AI agents, and sales teams are able to better target advertising based on user patterns.

Smart Assistants

Smart technologies can provide more direct assistance with how people interact with one another. From translating speeches to recommending nearby businesses, programs like Apple’s Siri are built to help human beings navigate the world around them.

Machine Learning Tomorrow

As we can see, machine learning is developing in interesting ways. As we become more and more reliant on digital enterprises, the uses for machine learning will only continue to grow. There are many advantages to these AI-driven programs, especially when it comes to the business world at large. Machine learning is practically tailor-made to improve the overall customer experience. The better a program can understand its users, the better recommendations it can provide. Many companies now use machine learning as guidance for their business model overall. Netflix, for example, takes an endless amount of user data to determine what the next “big thing” might be, using that to dictate the kind of entertainment that they ultimately greenlight.

The Premier Machine Learning Bootcamp

If all this sounds interesting to you, then you might want to consider a career in machine learning. Choosing this career path doesn’t have to be complicated. All you have to do is apply for the University of California at San Diego's Machine Learning Engineering and AI Bootcamp. This deeply informative program is designed to help its students get all the skills needed to competently join the industry, learning everything they need to know to be able to contribute to this rewarding, constantly growing field—and all in just a matter of months.