Artificial Intelligence (AI) and Machine Learning (ML) are among the most hyped terms in today’s world. Machine learning is a branch of computer science that gives AI the ability to learn tasks. To achieve this, programmers rely on machine learning algorithms.
The term machine learning is often confused with Artificial Intelligence, while it is actually a subfield of the latter. It is defined as the computer’s ability to learn without being explicitly programmed.
In its most basic form, machine learning uses programmed algorithms that serve to receive and analyze input data in order to predict output values within an acceptable range.
As new data is fed into these algorithms, they learn and optimize their operations to improve performance, developing “intelligence” over time.
What Are the Different Types of Machine Learning Algorithms?
There are different types of machine learning algos. Let’s have a look at them to better understand their contribution to making the world a convenient place to live in.
The primary purpose of regression algorithms is to estimate and understand the relationships between the variables. This type of ML algorithm focuses on one dependent variable and a number of other changing variables, which makes it particularly useful for prediction and forecasting.
This type of classification algorithms are based on Bayes’ theorem and classifies each value as independent of any other. It allows the prediction of a class or category based on a given set of characteristics using probability.
Despite its simplicity, the classifier works surprisingly well and is often used because it outperforms more sophisticated classification methods.
They are used in unsupervised learning and serve to categorize unlabeled data, that is, data without defined categories or groups.
The algorithm works by searching for clusters within the data, with the number of clusters being represented by the variable K. It then proceeds iteratively, assigning each data point to one of the K clusters based on the given features.
Decision Tree Algorithms
A decision tree is a flowchart-like tree structure that uses a branching method to illustrate each possible outcome of a decision. Within the tree, each node represents a test on a specific variable, and each branch is the result of that test.
Neural Network Algorithms
An artificial neural network (ANN) comprises units arranged in a series of layers, each of which connects to adjoining layers. ANNs are inspired by biological systems, such as the brain, and how they process information.
Therefore, they are essentially a large number of interconnected processing elements working in unison to solve specific problems.
They also learn by example and experience and are extremely useful for modeling nonlinear relationships in high-dimensional data or where the relationship between input variables is difficult to understand.
Dimension Reduction Algorithms
Dimension reduction reduces the number of variables considered to find the required information.
Deep Learning Algorithms
In deep learning algorithms, data is run through several layers of neural network algorithms. Each subsequent layer passes a simplified representation of the data to the next layer. Most work well on datasets that have up to a few hundred features or columns.
However, an unstructured dataset, such as an image, has such a large number of features that this process becomes cumbersome or completely infeasible.
Deep learning algorithms progressively learn more about the image as it passes through each layer of the neural network. The first few layers learn to detect low-level features like edges, and the later layers combine the features of the previous layers into a holistic representation.
What Are Some of the Major Areas Where Machine Learning Algorithms Can Be Used?
There’s no dearth of areas enjoying the immense benefits of machine learning algorithms, for example:
The financial services industry remains one of the biggest beneficiaries of machine learning. Thanks to ML’s unique ability to augment the processes with an unprecedented rate of success and accuracy.
What used to take humans hours, days, or even weeks can now be accomplished in minutes. American Express handles more than $1 billion in transactions on more than 110 million of its credit cards each year. They rely on machine learning to manage their data, uncover spending trends, and offer customers individualized offers.
Additionally, loan and credit card companies use machine learning to manage and predict risk. These algos allow them to consider an applicant’s credit history, along with thousands of other data points. For instance, mobile phone and rent payments to assess the lending company’s risk.
This kind of leverage enables lenders to offer loans to a much broader range of people who might not be able to get loans using traditional methods.
Trading companies are using machine learning to amass vast amounts of data and determine the optimal price points to execute trades. These complex, high-frequency trading algorithms take into account thousands, if not millions, of financial data points to buy and sell stocks at the right time.
Healthcare is another industry relying excessively on machine learning as a handy tool to discover new treatments, manage a plethora of medical information, and even predict and detect diseases.
Medical professionals can now easily view patient medical records without digging through tons of files or going through a long chain of communication with other areas of the hospital.
Up-to-date medical systems can now obtain pertinent health information for each patient in the blink of an eye.
AI and machine learning are projected to save the healthcare industry around $150 billion annually due to the time and resources they save on drug development. Machine learning-enabled artificial intelligence tools are working alongside drug developers to generate drug treatments at a faster rate than ever before.
Essentially, these machine learning tools feed into millions of data points and shape them in ways that help researchers see which compounds are successful and which are not. Machine learning proficiency spares millions of human hours on each trial, producing successful drug compounds in days, weeks, or months, depending upon the circumstances.
Similarly, machine learning has made disease detection and prediction much more accurate and faster. Right now, radiology and pathology departments around the world are employing machine learning to analyze CT scans and X-rays and find diseases.
After receiving thousands of disease images through a combination of supervised, unsupervised, or semi-supervised algorithms, some machine learning systems have been trained to detect and diagnose diseases (such as cancer or viruses) at a faster rate than humans.
There are various other avenues where machine learning has also been used to predict deadly viruses, such as Ebola and malaria, and is used by the CDC to track cases of the flu virus each year.
A handful of AI & ML ventures like RevolveAI are coming up with other innovative machine learning apps helping healthcare setups and governments in saving millions of dollars by taking preventive actions. For instance, they designed an ML app capable of tracking and identifying people not wearing masks properly, generating alerts to intimate the authorities.
Social media companies are employing machine learning for two main reasons. First, they want to create a sense of community and weed out bad actors and malicious information. Machine learning encourages the former when looking at pages, tweets, topics, etc., an individual likes and suggests other topics or community pages based on those likes. Basically, it counts on your preferences as a medium to power a social media recommendation engine.
The massive surge of “fake news” in the 2016 election prompted social media outfits like Facebook and Twitter to put machine learning at the forefront of their systems. Machines are simply faster than humans at identifying fake news and taking it down before it becomes a problem.
Both Twitter and Facebook are using updated computer systems to quickly identify harmful patterns of misinformation, flag bad bots, view reported content, and remove it when necessary to build truth-based online communities.
Retail and e-Commerce
The retail industry is also catching up quickly in exploiting the potential of machine learning for its ability to optimize sales and collect data on individualized shopping preferences.
Machine learning gives retailers and online stores the ability to make purchase suggestions based on clicks, likes, previous purchases, etc., of a user.
Once customers feel that retailers understand their needs, they are less likely to move away from that company and will buy more items. This use of machine learning increases customer satisfaction and maximizes profits for retailers.
Visual search is becoming a big part of the shopping experience. Instead of typing queries, customers can now upload an image to show the computer exactly what they’re looking for. Machine learning will analyze the image (using layers) and produce search results based on its findings.
For example, you can upload a photo of a red sweater that you found on Instagram. From there, the machine learning-based system will extract that exact sweater and then other suggestions based on the same look in milliseconds.
Predicting customer trends and behavior has also benefitted greatly from contemporary machine learning prowesses. These machines will comprehensively analyze individual purchases to determine what types of items are selling (and what items will be sold in the future).
For example, perhaps a new food has been deemed a “superfood.” A grocery store’s systems can identify larger purchases of that product and can send customers coupons (or create targeted ads) for all variations of that item. Additionally, a system could analyze individual purchases to send you future coupons.
What are Some of the Current Applications of Machine Learning Algorithms?
Machine learning is being used in many ways, and its applications will continue to increase as the importance of using data as valuable information in all sectors is understood. It is important to understand that the way in which we interpret information is capable of helping in decision-making, bringing us closer to solving problems in a more efficient way.
Here is a list of top applications relying on machine learning algorithms.
Various algorithms are used to understand images (especially deep learning) and find something in particular or group areas. We know that images are sets of continuous pixels, and each one contains information about the color that it has to “illuminate” (for example, RGB).
Optical Character Recognition (OCR) is one common example. It involves finding letters, grouping them, finding spaces, and being able to decipher texts. Another example is detecting people in images and human presence in security cameras. Remember, Facebook is able to locate your cousins and siblings in your photo albums?
From the sound waves synthesized by the microphone of your computer, smartphone, or your car’s audio, machine learning algorithms are capable of cleaning noise, intuiting the silences between words, and understanding your language to interpret your commands. Whether it is “Siri, add a reminder for next Monday”, “Hey Google, Play Coldplay music”, or even “Make reservations and order pizza”, speech recognition is there. Part of the recognition is done through Natural Language Processing (NLP).
It consists of identifying to which Class each individual of the population that we are analyzing belongs. It will be assigned a discrete value of type 1, 0 as in the classification of Spam or not. It could also be used to classify whether a tumor is benign or not or to classify flowers according to the characteristics obtained.
Regression is a machine learning technique used to predict future events. It is based on the assumption that past events allow predictions about future events. Regression can be used to predict anything from the weather to stock prices to the outcome of elections.
One of the most common uses of regression is to predict the future sales of a product. This can be done by analyzing past sales data to identify patterns. For example, if past sales data shows that sales of a product increase when the temperature is above a certain threshold, then the regression algorithm can be used to predict future sales of the product based on the current temperature.
Unsupervised learning is often used to create and discover unknown patterns in the behavior of customers of a website, app, or business. These algorithms can discover groups that we were completely unaware of or certain group characteristics that correlate and that we would have hardly identified.
Much of the research in ML is being carried out in this field to give cars a life of their own. Some of the uses will be for the car to move the steering wheel by itself, analyze the images, detect other vehicles, not collide and even predict how others are moving to avoid accidents.
A car has a lot of decisions to make, and in a very short time, all of which are crucial and critical.
We watch a movie or a series. Millions of people watch that same series. Wouldn’t it be logical for us to like another movie that other users with a profile similar to ours saw? Well, that is what Netflix and other providers take advantage of to be able to recommend content to their users.
Amazon is famous for its recommendations like, “If you buy that product, you will also be interested in this other one”. It is also achieved with this type of Machine Learning application.
Some Common Examples of Apps Using Machine Learning Algorithms
There are many, but we’ll confine to the most prominent ones in this passage here.
Netflix, the streaming media and video app uses machine learning to deliver a better user experience and improve engagement. Netflix uses machine learning to cater to user preferences, choices, and intentions based on their activities.
Tinder, the world’s most popular dating app, has already broken all records for user engagement and satisfaction among all other dating apps. Tinder also uses a machine learning algorithm to understand user intent and preferences more accurately and figure out how to show users a profile that’s likely to swipe right.
Machine learning is not just about giving customers perfect recommendations to ensure consistent sales performance. Snapchat is one of the few successful apps that has used the full capabilities of machine learning technology. Filters like 3D Paint on Snapchat are great examples of how AR (augmented reality) and ML can be used together to improve computer vision.
Google Maps’ use of Machine Learning is another prominent example of how this technology can ensure optimal efficiency and usability for end-users. Instead of waiting for a user’s input and command each time, Google Maps uses ML to predict the possible action a user would intend under particular circumstances.
Spotify is an application that allows you to enjoy music digitally in a very satisfying way. Part of its success lies in the user experience, which uses machine learning to take into account your preferences to make similar suggestions.
It analyzes the trends and tastes of a community to maintain that content in a preferential way and offer it more regularly.
One of the visual platforms that have been considered very intuitive to navigate for a long time because the application uses artificial intelligence to keep its users anchored.
Machine learning algorithms help Pinterest interpret the patterns in the images and take user searches into account to offer content based on the place or country of residence.
It also parses the captions and descriptive information of the image to associate. Each and every one of them is used to prioritize in addition to offering a catalog of images based on the above. All this has resulted in it being an application that adapts to the person who is using it.
What other applications of Machine Learning do you know? Do you use Artificial Intelligence in your work, studies, or research? Share your experiences and aspirations with us to further enrich the discussion on this intriguing topic.