Today, machine learning and artificial intelligence (AI) is everywhere. And whether you know it or not you must have used AI one way or the other – think about your phone and the apps you use. So what is machine learning really?
Here, we will examine how machine learning is defined as a tool used by data scientists and how it is currently being used, and what lies ahead as it continues to evolve.
Definition of Machine Learning
The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze data sets and identify patterns Machine learning techniques leverage data mining to identify historic trends to inform future models.
The typical supervised machine learning algorithm consists of (roughly) three components:
- A decision process: A recipe of calculations or other steps that takes in the data and returns a “guess” at the kind of pattern in the data your algorithm is looking to find.
- An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
- An updating or optimization process: Where the algorithm looks at the miss and then updates how the decision process comes to the final decision so that the next time the miss won’t be as great.
For example, if you’re building a movie recommender, your algorithm’s decision process might look at how similar a given movie is to other movies you’ve watched and come up with a weighting system for different features.
During the training process, the algorithm goes through the movies you have watched and weights different properties. Is it a sci-fi movie? Is it funny? The algorithm then tests out whether it ends up recommending movies that you (or people like you) actually watched. If it gets it right, the weights it used stay the same; if it gets a movie wrong, the weights that led to the wrong decision get turned down so it doesn’t make that kind of mistake again.
Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention — providing the ability to uncover hidden insights without being specifically programmed to do so.
What Are Some Machine Learning Methods?
Many machine learning models are defined by the presence or absence of human influence on raw data — whether a reward is offered, specific feedback is given or labels are used.
- Supervised learning: The dataset being used has been pre-labeled and classified by users to allow the algorithm to see how accurate its performance is.
- Unsupervised learning: The raw dataset being used is unlabeled and an algorithm identifies patterns and relationships within the data without help from users.
- Semisupervised learning: The dataset contains structured and unstructured data, which guide the algorithm on its way to making independent conclusions. The combination of the two data types in one training dataset allows machine learning algorithms to learn to label unlabeled data.
- Reinforcement learning: The dataset uses a “rewards/punishments” system, offering feedback to the algorithm to learn from its own experiences by trial and error.
Finally, there’s the concept of deep learning, which is a newer area of machine learning that automatically learns from data-sets without introducing human rules or knowledge. This requires massive amounts of raw data for processing and the more data that is received, the more the predictive model improves.
Why Is Machine Learning Important?
Machine learning and data mining, a component of machine learning, are crucial tools in the process to glean insights from massive datasets held by companies and researchers today. There are two main reasons for this:
- Scale of data: Companies are faced with massive volumes and varieties of data that need to be processed. Processing power is more efficient and readily available. Models that can be programmed to process data on their own, determine conclusions, and identify patterns are invaluable.
- Unexpected findings: Since machine learning algorithms update autonomously, the analytical accuracy improves with each run as it teaches itself from the datasets it analyzes. This iterative nature of learning is unique and valuable because it occurs without human intervention, providing the ability to uncover hidden insights without being specifically programmed to do so.
Who Is Using Machine Learning?
Companies leveraging algorithms to sort through data and optimize business operations aren’t new.
- Marketing and sales
- Financial services
- Brick-and-mortar retail
- Oil and gas
Amazon, Facebook, Netflix, and, of course, Google have all been using machine learning algorithms to drive searches, recommendations, targeted advertising, and more for well over a decade.
What Is the Future of Machine Learning?
Machine learning algorithms are being used around the world in nearly every major sector, including business, government, finance, agriculture, transportation, cybersecurity, and marketing. Such rapid adoption across disparate industries is evidence of the value that machine learning (and, by extension, data science) creates. Armed with insights from vast datasets — which often occur in real time — organizations can operate more efficiently and gain a competitive edge.
The continued digitization of most every sector of society and industry means that an ever-growing volume of data will continue to be generated. The ability to gain insights from these vast datasets is one key to addressing an enormous array of issues — from identifying and treating diseases more effectively, to fighting cyber criminals, to helping organizations operate more effectively to boost the bottom line.
The universal capabilities that machine learning enables across so many sectors makes it an essential tool — and experts predict a bright future for its use.
“AI and machine learning … can also encompass more advanced systems that understand, learn, predict, adapt and potentially operate autonomously … . The combination of extensive parallel processing power, advanced algorithms and massive data sets to feed the algorithms has unleashed this new era.”
As machine learning and AI applications are becoming more popular, they’re becoming more accessible too — moving from server-based systems to the Cloud. Over the past few years, Google, Amazon, Microsoft, and IBM all unveiled machine learning platforms through open source projects and enterprise cloud services.
Now you know what machine learning does, you can begin to look for its most useful applications for you and/or your business.