What Is Machine Learning? What Are Machine Learning Algorithms? What Are Popular Machine Learning Examples?
Machine learning is a subset of artificial intelligence that gives systems the ability to learn and optimize processes without having to be consistently programmed. Simply put, machine learning uses data, statistics and trial and error to “learn” a specific task without ever having to be specifically coded for the task.
What is Machine Learning?
Machine learning is a very promising subfield of artificial intelligence, where systems have the ability to “learn” through data, statistics and trial and error in order to optimize processes and innovate at quicker rates. Machine learning is giving computers the ability to develop human-like learning capabilities that are allowing them to solve some of the world’s toughest problems, ranging from cancer research to climate change.
How, exactly, is machine learning making computers more human-like? Most computer programs rely on code to tell them what to execute or what information to retain (better known as explicit knowledge). This knowledge contains anything that is easily written or recorded, like textbooks, videos or manuals. With machine learning, computers are now gaining tacit knowledge, or the knowledge we gain from personal experience and context. This type of knowledge is hard to transfer from one person to the next via written or verbal communication.
For example, facial recognition is a type of tacit knowledge. We recognize a person’s face, but it is hard for us to accurately describe how or why we recognize it. We rely on our personal knowledge banks to tacitly connect the dots to immediately recognize a person based on their face. Another example is riding a bike. It’s much easier to show someone how to ride a bike than it is to explain it.
Computers no longer have to rely on billions of lines of code to carry out calculations. Machine learning gives computers the power of tacit knowledge that allows these machines to make connections, discover patterns and make predictions based on what it learned in the past. Machine learning’s use of tacit knowledge has made it a go-to technology for almost every industry from fintech to weather and government.
What is Deep Learning?
Deep learning is a subfield within machine learning gaining traction for its unique ability to extract data with high accuracy rates. It uses Artificial Neural Networks (ANNs) to extract higher level features from raw data. ANNs, though much different from human brains, were inspired by the way humans biologically process and information. The learning a computer does is considered “deep” because the networks use layering to learn from, and interpret, raw information.
For example, deep learning is an important asset for image processing in everything from e-commerce to medical imagery. Google is equipping its programs with deep learning to discover patterns in images in order to display the correct image for whatever you search. If you search for a winter jacket, Google’s machine and deep learning will team up to discover patterns in images (sizes, colors, shapes, relevant brand titles, etc.) that display pertinent jackets that satisfy your query. Deep learning is also making headwinds in radiology, pathology and any medical sector that relies heavily on imagery. The technology relies on its tacit knowledge (from studying millions of other scans) to immediately recognize disease or injury, saving doctors and hospitals both time and money.
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Popular Types of Machine Learning Algorithms
Like all systems with AI, machine learning needs algorithms to establish parameters, actions and end values. Machine learning-enabled programs use these algorithms as a guide when it explores different options and evaluates different factors. There are hundreds of algorithms computers use based on several factors like data size and diversity. Below are a few of the most popular types of machine learning algorithms.
Supervised learning algorithms build mathematical models of data that contain both input and output information. Supervised learning algorithms are called training data because the program knows the beginning and end results of the data. It just has to figure out how to most efficiently get to the end result. Machine learning computer programs are constantly fed these sets of algorithms, so the programs can eventually predict outputs based on a new set of inputs.
Regression and classification algorithms are two of the more popular supervised learning algorithms. Regression analysis is used to discover and predict relationships between outcome variables and one or more independent variables. Commonly known as linear regression, this algorithm is used as training data to help systems with predicting and forecasting. Classification algorithms are used to train systems on identifying an object and placing it in a sub-category. For instance, email filters use machine learning to automate incoming email flows for primary, promotion and spam inboxes.
Unsupervised learning algorithms take data only containing inputs and then add structure to the data in the form of clustering or grouping. The algorithms learn from previous test data that hasn’t been labeled or categorized and will then group the raw data based on commonalities (or lack thereof). Cluster analysis uses unsupervised learning to sort through giant lakes of raw data to group certain data points together. Clustering is a popular tool for data mining, and it is used in everything from genetic research to creating virtual social media communities with like-minded individuals.
Semi-supervised learning falls directly in between unsupervised and supervised learning. Instead of giving a program all labeled data (like in supervised learning) or no labeled data (like in unsupervised learning), these programs are fed a mixture of data that not only speeds up the machine learning process, but helps machines identify objects and learn with an increased accuracy.
Typically, programmers introduce a small number of labeled data in with a large percentage of unlabeled information and the computer will have to use the groups of structured data to cluster the rest of the information. This is seen as a promising algorithm because labeling supervised data is seen as a massive undertaking because of high costs and hundreds of hours spent.
Here is a handy way to remember machine learning algorithms in layman’s terms. Supervised learning is like being a student and having the teacher constantly watch over you at school and at home. Unsupervised learning is telling a student to figure a concept out themselves. Semi-supervised learning is like giving a student a lesson and then testing them on questions pertinent to that topic. Each algorithm type has its advantages and disadvantages in machine learning, and are used based on the parameters and needs of the data scientist or engineer.
Machine Learning Examples & Applications
The financial services industry is championing machine learning for its unique ability to speed up processes with a high rate of accuracy and success. The technology is being employed in virtually every aspect of our financial systems. What has taken humans hours, days or even weeks to accomplish can now be executed in minutes. American Express handles over $1 trillion in transactions from more than 110 million of their credit cards each year. The company relies on machine learning to manage their data, discover spending trends and offer customers individualized offers.
Additionally, machine learning is used by lending and credit card companies to manage and predict risk. These computer programs take into account a loan seeker’s past credit history, along with thousands of other data points like cell phone and rent payments, to deem the risk of the lending company. By taking other data points into account, lenders can offer loans to a much wider array of individuals who couldn’t get loans with traditional methods.
Trading firms are using machine learning to amass a huge lake of data and determine the optimal price points to execute trades. These complex high-frequency trading algorithms take thousands, if not millions, of financial data points into account to buy and sell shares at the right moment.
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The healthcare industry is championing machine learning as a tool to manage medical information, discover new treatments and even detect and predict disease. Medical professionals, equipped with machine learning computer systems, have the ability to easily view patient medical records without having to dig through files or have chains of communication with other areas of the hospital. Updated medical systems can now pull up pertinent health information on each patient in the blink of an eye.
AI and machine learning are predicted to save the healthcare industry around $150 billion annually because of the time and resources they save in drug development. Machine learning-enabled AI tools are working alongside drug developers to generate drug treatments at faster rates than ever before. Essentially, these machine learning tools are fed millions of data points, and they configure them in ways that help researchers view what compounds are successful and what aren’t. Instead of spending millions of human hours on each trial, machine learning technologies can produce successful drug compounds in weeks or months.
Machine learning has made disease detection and prediction much more accurate and swift. Right now, machine learning is being employed by radiology and pathology departments all over the world to analyze CT and X-RAY scans and find disease. After being fed thousands of images of disease through a mixture of supervised, unsupervised or semi-supervised algorithms, some machine learning systems are so advanced that they can catch and diagnose diseases (like cancer or viruses) at higher rates than humans. Machine learning has also been used to predict deadly viruses, like Ebola and Malaria, and is used by the CDC to track instances of the flu virus every year.
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Machine learning is being employed by social media companies for two main reasons: to create a sense of community and to weed out bad actors and malicious information. Machine learning fosters the former by looking at pages, tweets, topics, etc. that an individual likes and suggesting other topics or community pages based on those likes. It’s essentially using your preferences as a way to power a social media recommendation engine.
The massive spread of “fake news” in the 2016 election prompted social media companies, like Facebook and Twitter, to put machine learning at the forefront of their systems. Machines are simply faster than humans at identifying false news and deleting it before it ever becomes a problem. Both Twitter and Facebook are using upgraded computer systems to quickly identify harmful patterns of false information, flag malicious bots, view reported content and delete when necessary in order to build online communities based on truth.
Retail and E-commerce
The retail industry is quickly relying on machine learning for its ability to optimize sales and gather data on individualized shopping preferences. Machine learning offers retailers and online stores the ability to make purchase suggestions based on a user’s clicks, likes, past purchases, etc. Once customers feel like retailers understand their needs, they are less likely to stray away from that company and will purchase more items. This use of machine learning boosts customer satisfaction, while maximizing profits for retailers.
Visual search is becoming a huge part of the shopping experience. Instead of typing in queries, customers can now upload an image to show the computer exactly what they’re looking for. Machine learning will analyze the image (using layering) and will produce search results based on its findings. For instance, you may upload a photo of a red sweater you found on Instagram. From there, the machine learning-based system will pull up that exact sweater and then other suggestions based on the same look within milliseconds.
Machine learning has also been an asset in predicting customer trends and behaviors. These machines will look holistically and at individual purchases to determine what types of items are selling (and what items will be selling in the future). For example, maybe a new food has been deemed a “super food.” A grocery store’s systems might identify increased purchases of that product and could send customers coupons (or create targeted advertisements) for all variations of that item. Additionally, a system could look at individual purchases to send you future coupons. Maybe, for example, you’ve been browsing newborn baby clothes. A retailer’s machine learning systems will identify that you are pregnant or a new parent and offer you items that it thinks would be helpful to your new child.
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When productionizing machine learning models, take the time to automate and standardize as many parts of the process as possible.