Imagine asking a computer to identify a picture of a cat or a dog without any supporting data; the computer has a 50/50 chance of being correct in this scenario. Now, imagine writing a program that teaches a computer how to actively learn the difference between the animals by analyzing photos of both.
This is the essence of machine learning. As humans, we learn from experience while machines generally follow our instructions. However, with machine learning, we can train computers to learn from data and perform high-level analyses and predictions. Machine learning is one of modern technology’s most promising concepts, one with boundless applications across most industries.
If you’re interested in a technology-related career, there’s a good chance that a working knowledge of machine learning will make you more marketable. In fact, some jobs focus specifically on incorporating machine learning advancements in order to help businesses gain a competitive advantage
What Is Machine Learning?
The simplest machine learning definition is this: the science of teaching computers how to learn like humans. Machine learning requires algorithms to examine huge datasets, find patterns within that data, and then make assessments and predictions based on those patterns. Essentially, it is a branch of artificial intelligence (AI) that shifts the rules of programming as we conventionally understand them.
Normally, programmers write programs where they input data and rules and the computer follows those rules to produce an answer. With machine learning, programmers input the data and the answer, and the computer determines the rules for producing that answer. In the earlier pet picture example, programmers would input the answer (“This is a photo of a cat”), the data (photos of cats and dogs), and the computer would use an algorithm to learn the difference.
Machine learning is applied in many familiar ways. Your favorite streaming service uses machine learning to recommend movies and shows based on your viewing habits; financial institutions use it to spot fraud in billions of transactions and devise ways to prevent it; self-driving cars use it to learn directional commands; and phones use it to enact accurate facial recognition.
According to a 2020 study, the global size of the machine learning market was valued at $6.9 billion in 2018. It is projected to increase nearly 44 percent through 2025 as companies seek to optimize their supply chains and use more digital resources to reach customers.
To be effective, machine learning needs detailed pieces of data from diverse sources. Algorithms learn best when they can apply vast amounts of data to a specific model. For example, the more photos of dogs and cats you input, the better the algorithm will become in identifying the differences between the animals.
The term “machine learning” is often used synonymously with artificial intelligence and, while these concepts share similarities, they are generally used for different purposes. AI is the broad science of training machines to perform human tasks, while machine learning is one of many AI-based methods of accomplishing that training.
Machine Learning Algorithm Types
Algorithms are the procedures that computers use to perform pattern recognition on data models and create an output. Many types of algorithms exist, and they fall into four primary groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Supervised learning is a process where labeled input data and the correct answers are both given to a computer so that it can learn how to reach the correct answer on its own. The correct answer, or output, can refer to an object, a situation, or a problem that needs to be solved.
There are two types of supervised learning: classification and regression. Classification is simply the process of sorting identified data into groups. To illustrate, let’s apply classification to our cats and dogs example: The programmer inputs labeled photos of cats and dogs so the computer knows which photo shows which type of animal. Using that training data set to learn how to identify the pictures, the computer can then apply its knowledge to a new data set and label them correctly. The more photos the computer analyzes, the faster and more accurate it becomes at classifying the data.
The second type of supervised learning is regression, which enables the computer to forecast likely future or desirable outcomes from a labeled data set. Different types of regression are used to forecast future sales, anticipated stock performanced, or the impact of financial events on the global economy. However, regression can be used for far more than financial analysis.
Here are some other examples:
Facebook offers you a friend suggestion because it recognizes your friend in a photo album of tagged pictures.
Netflix recommends movies for you to watch based on your past viewing choices, ratings, and the viewing habits of subscribers with similar tastes.
Predicting Home Prices
Realtors use machine learning to analyze housing data (location, square footage, number of bedrooms, added features like pools, etc.) in comparison to other properties to understand not only how to best price a property, but also how that price will impact days on the market and likely closing dates for their clients.
Suggesting Further Purchases
When you check out a cart full of items at the grocery store, those purchases become data. Retailers use that data in many ways — such as predicting future purchases, making suggestions, or offering coupons as incentives.
Unsupervised learning refers to the process in which a computer finds nonintuitive patterns in unlabeled data. It’s different from supervised learning because the datasets are not labeled and the computer is not given a specific question to answer.
There are many different types of unsupervised learning including K-means clustering, hierarchical clustering, anomaly detection, and principal component analysis to name a few. The most commonly discussed uses are clustering and anomaly detection.
Clustering is used to find natural groups, or clusters, within a dataset. These clusters can be analyzed to group like customers together (e.g, customer segmentation), identify products that are purchased at the same time (e.g., peanut butter and jelly), or better understand the attributes of successful executives (e.g., technical skills, personality profile, education).
In our dogs and cats example, assume you input pictures of dogs and cats but don’t label them. Using clustering, the computer will look for common traits (body types, floppy ears, whiskers, etc.) and group the photos. However, while you may expect the computer to group the photos by dogs vs. cats, it could group them by fur color, coat length, or size. The benefit of clustering is that the computer will find nonintuitive ways of looking at data which enable the discovery of new data trends (e.g., there are twice as many long-coated animals as short-coated) which allow for new marketing opportunities (e.g., dry pet shampoo and brush marketing increases).
In anomaly detection, however, the computer looks for rare differences rather than commonalities. For example, if we used anomaly detection on our dog and cat photos, the computer might flag the photo of a Sphynx cat because it is hairless or an albino dog due to its lack of color.
Here are some other applications of anomaly detection.
Banks analyze all sorts of transactions: deposits, withdrawals, loan repayments, etc. Unsupervised learning can group these data points and flag outlier transactions (e.g., transactions that don’t align with the majority of data points) that may indicate fraud.
Companies use anomaly detection to identify and understand actions competitors may take in the marketplace. For example, a retailer may expect to take three share points in every new market they open a store during the first month of operations; however, they may notice certain new stores are underperforming and don’t know why. Anomaly detection can be used to identify likely competitive activity which is preventing share growth. Specifically, the anomaly of common products not being found in their shoppers’ baskets (e.g., bread, milk, eggs, chicken breast) which may indicate covert competitor incentives that are successfully impacting the retailer’s shopper frequency and average order size.
Computers use unsupervised learning to perform all sorts of image recognition tasks including facial recognition to open your mobile phone and healthcare imaging where identifying cell-structure anomalies can assist in cancer diagnosis and treatment.
Semi-supervised learning is essentially a combination of supervised and unsupervised learning techniques. It merges a small amount of manually labeled data (a supervised learning element) as a basis for autonomously defining a large amount of unlabeled data (an unsupervised element). Through data clustering, this method makes it possible to train a machine learning algorithm (ML algorithm) on data annotation (e.g., the labeling or classification of data) without manually labeling all of the training data first, potentially increasing efficiency without sacrificing quality or accuracy.
For example, if you have a large data set consisting of dogs and cats, a semi-supervised approach would allow you to manually label a small portion of that data (identifying a few pictures as “dogs” and a few others as “cats”), and the ML algorithm would then be equipped to properly define the remaining data. This blends the benefits of supervised and unsupervised learning by nudging the algorithm to make strong autonomous decisions with less initial human oversight.
While higher-level image classification often requires a fully supervised approach (due to the necessary labeling of a large amount of initial training data), specific image classification scenarios can benefit from semi-supervised learning. For example, to annotate images of handwritten numbers, training data must be clustered to include the most representative variations of the written numbers and can then be used to inform the ML algorithm. In turn, the algorithm should be able to identify unlabeled images of handwritten numbers with relatively high accuracy, yielding the intended outcome with less initial oversight.
Similarly, semi-supervised learning can be useful in document classification, eliminating the need for human workers to read through numerous text documents just to broadly classify them. A semi-supervised approach allows the algorithm to learn from a relatively small amount of text data so that it can identify and classify the larger amount of unlabeled documents.
Reinforcement learning is the process by which a computer learns how to behave in a certain environment by performing an action and seeing a specific result. In this process, the key terms to know are agents and environments. Agents interact with the environment through actions and receive feedback regarding those actions. Consider it similar to the first time you (the agent) touched a hot stove (the environment) — the feedback from the action (e.g., pain of touching the stove) reinforced the idea that you shouldn’t touch a hot stove again.
Reinforcement can also be applied to our cats and dogs scenario. If you input an image of a dog and the computer says it’s a cat, you can then correct that answer. The computer will learn from that correction, or reinforcement, and increase its ability to properly identify the image over time and through repetition of the process.
Reinforcement is a growing method of machine learning because of its applications in robotics and automation. Consider these examples:
Autonomous vehicles interpret a huge amount of data through cameras, sensors, and radar that monitor their surroundings. Reinforcement learning contributes to the real-time decision-making process.
Companies automate tasks in warehouses and production facilities through robotics that operate on reinforcement learning models.
Reinforcement learning is becoming more common in medicine because its methodology (e.g., learning from interactions in an environment) often mirrors that of diagnosis and treating diseases.
Reinforcement learning algorithms are popular for video games because they learn quickly and can mimic human performance. Reinforcement is one way computers learn how to master games from chess to complex video games, allowing bot players to engage with human players in a realistic way.
Machine Learning Jobs
As we look to automate more processes at work and in our daily lives, machine learning will become more valuable. Machine learning is important to data science, artificial intelligence, and robotics (among many other fields).
Where can knowledge of machine learning take you? Here are a few potential careers to consider.
- Machine Learning Engineer: Though coding is required, this role is a bit different than that of a computer programmer. Machine learning engineers build programs that teach computers how to identify patterns and perform tasks based on those patterns. This is an ideal career path for those who want to get into robotics.
- Machine Learning Data Scientist: Data scientists combine statistics, programming, and data analysis to generate insight from data — a skill that is in high demand. According to the U.S. Bureau of Labor Statistics (BLS), the demand for computer and information research scientists is expected to grow by 15 percent through 2029. Machine learning is an important component of becoming a data scientist.
- NLP Scientist: When you ask Siri or Alexa a question, they answer because of natural language processing (NLP). NLP scientists work in a unique world of textual data analysis, linguistics, and computer programming to facilitate communication between humans and machines.
- Business Intelligence Developer: Companies need ways to harness, assess, and report all the data they collect, and business intelligence (BI) provides that framework. BI developers work with data warehouses, visualization software, and other tools to explain what is happening. BI is also vital in generating ways to benefit from consumer data.
- Data Analyst: Want to help companies and organizations make sense of their data? Then you may want to consider becoming a data analyst. You’ll learn how to mix statistics, business knowledge, and communication skills to bring data to life. As data generation grows, so do the job prospects for analysts. The BLS projects a 25 percent increase by 2029.
Want to learn more about the function of machine learning in data science? Check out this guide to understanding data science roles.
Machine Learning FAQs
What is machine learning like for beginners?
Machine learning requires a solid foundation in fields like math, statistics, and programming. Calculus and linear algebra are important starting points, as is the ability to code. A great way to learn about machine learning, and other data science skills, is to enroll in a data science boot camp.
What is a good introduction to machine learning?
What is an example of machine learning?
Ready to take the next step in a career that involves machine learning? Consider a bootcamp in data science and analytics. The 24-week online program at Georgia Tech Data Science and Analytics Boot Camp is a great way to learn in-demand skills to get you ready for your job search. Contact us today to get started.