What Is Machine Learning?

Machine learning is a method of data analysis and a branch of artificial intelligence that is based on the idea that systems can learn from data, make decisions and perform tasks without explicitly being told to do so.

In machine learning, algorithms—which are a sequence of statistical processing steps—are “trained” to find patterns and features in massive amounts of data and “learn” to make future decisions and predictions based on the data. The better the algorithm, the more accurate the decisions and predictions will become.

“Machine learning is a core, transformative way by which we’re rethinking everything we’re doing.”

-Google CEO Sundar Pichai

Everyday Applications of Machine Learning

In the past decade, the pace of technological innovation has increased dramatically, and advances in machine learning have grown exponentially, affecting everything from our choices in entertainment to our personal finances. Some applications of machine learning include:

  • Search-engine rankings (Google, Baidu)
  • Social network behavior predictions (Facebook, Twitter)
  • Virtual personal assistants (Siri, Alexa)
  • Instantaneous financial risk evaluation (credit card companies)
  • E-commerce personalization (Amazon)
  • Entertainment recommendation systems (Netflix, YouTube, Spotify)
  • Self-driving cars
  • Rational drug design
  • Weather and traffic prediction

Types of Machine Learning Algorithms

There are three major types of machine learning algorithms. They can be divided into the following categories:

Supervised Learning

These algorithms involve direct supervision, with the developer setting strict boundaries on the algorithm to use incoming data to assess possible outcomes.

Example: Supervised learning is commonly used for price prediction and trend forecasting in sales, retail commerce and stock trading.

Unsupervised Learning

These algorithms describe information by sifting through data and making sense of it. They do not involve the direct control of the developer because the desired results are unknown and yet to be defined.

Example: Unsupervised learning is commonly used in modern data management to identify target audience groups based on certain credentials.

Reinforcement Learning

Commonly understood as machine learning artificial intelligence, reinforcement learning algorithms develop a self-sustained system that, through contiguous sequences of tries and fails, improves itself.

Example: Reinforcement learning is commonly used in certain types of video games and in self-driving cars.

Are You Ready to Become a Machine Learning Engineer?

Learn more about the online Master of Science in Data Science from the University of Denver today.

Machine Learning Careers and Salaries

Machine learning engineers are in high demand. The profession is growing at a tremendous rate, with companies looking for experts who can turn their data into action by helping them make swift and accurate business decisions.

#1

Machine learning engineer is ranked as the No. 1 emerging job by LinkedIn (2020).1 and No. 1 on the best jobs list by Indeed.com (2019).2

$114,000

Jobs specifying machine learning skills pay an average of $114,0003

$30.6 billion

The global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 20243

Machine learning has practical applications that impact our lives in many ways and across a wide range of industries, including:


Consumer goods

Apply machine learning for everything from programming a talking doll to improving a beverage company’s target marketing.


Financial services

Apply machine learning to help detect fraud and connect customers with the right products and services.


Health care

Apply machine learning to review scans and help diagnose and treat patients.


Media

Apply machine learning to help predict what customers will enjoy listening to, reading or watching.

Machine Learning Online and DataScience@Denver

As the world of data science continues to evolve, the DataScience@Denver program evolves along with it, placing emphasis on the quantitative, programmatic and machine learning aspects of the field.

In fact, with the rise in prominence of machine learning, we have given the subject an expanded footprint, with coursework that incorporates machine learning theory, techniques and application throughout the curriculum. At DataScience@Denver, our curriculum goes beyond theory—through hands-on learning, our students are able to apply machine learning concepts and techniques taught in class to solve real-world business problems.

Course Spotlights

COMP 4432
Machine Learning

This course provides an overview of machine learning techniques and the problems they are designed to solve. This includes the broad differences between supervised/unsupervised and reinforcement learning, as well as associated learning problems such as classification and regression. Techniques covered may include approaches such as linear and logistic regression, neural networks, support vector machines, kNN, decision trees, random forests, Naive Bayes, EM, k-Means and PCA.

COMP 4431
Data Mining

Data mining is the process of extracting useful information that is implicitly hidden in large databases. This course is an introduction to the various techniques and underlying mathematical principles—from statistics to artificial intelligence—that are used to discover hidden patterns in massive collections of data. Topics covered include basic data analysis, frequent pattern mining, clustering, classification and model assessment.

Review the DataScience@Denver curriculum.

Whether you choose to use your Master of Science in Data Science degree to become a data scientist or a machine learning engineer, you’ll graduate from our program with a robust machine learning foundation that will help you propel your career forward.

Get Started

Join an industry that’s growing exponentially.4 Request information about DataScience@Denver today.