Different Types of Machine Learning You Should Know

Machine learning is an artificial intelligence (AI) process that allows computers to learn much as humans do. Businesses use it for many purposes. Machine learning types rely on labeled and unlabeled data types to train them. Others provide “rewards” to the algorithm for reaching decisions.

Artificial intelligence (AI) is changing the business world significantly. Recent developments in AI writing technology and art have led to questions about the ethics of using AI for content creation .?

One of these AI processes, machine learning, allows businesses to perform tasks and make decisions faster and at an enormous scale. Machine learning is vital for organizations, but what does it entail? More importantly, is it a good fit for every business????

What Is Machine Learning?

Machine learning describes the practice of artificial intelligence that focuses on how data and algorithms can imitate how humans learn. Machine learning has its roots in the 1959 studies of Arthur L. Samuel . Samuel coined the term when describing how an IBM 7094 computer defeated a self-proclaimed checkers master, Robert Nealey.

Machine learning today looks a lot different. When we talk about machine learning, we describe the process of training algorithms to make predictions or classifications and uncover critical insights through data mining. Businesses use these insights to make decisions, ideally to impact growth metrics.

At its core, machine learning describes how we give computers the ability to learn new things without explicit programming .

How Is Machine Learning Used?

Machine learning has practically limitless applications, but some are more advanced than others. While it is prevalent in tech companies and digital businesses, nearly any organization that relies on or gathers data can benefit. The following industries are major proponents and adopters of machine learning :

• Sales and marketing
• Financial services
• Retail
• Healthcare
• Transportation
• Government

Of course, tech companies are the most prominent players in the machine-learning and algorithm usage space. Amazon, Netflix, Facebook, Google and others have been using these systems to target users with advertising and recommendations for years.???

Use Cases of Machine Learning in the Business World

Machine learning allows businesses to perform tasks at a previously unimaginable scale. By harnessing machine learning, organizations can become more efficient in fields as diverse as customer service, finance and fraud detection.?

Some of the most common uses of machine learning in the business space include the following:

• Real-time chatbots: These machine learning processes allow users to “chat” with machines to ask questions and engage with organizations. Early chatbots required a script that allowed them to take action, but natural language processing (NLP) will enable chatbots to function more naturally.
• Decision support: Algorithms trained on historical data can run multiple scenarios simultaneously and help businesses and clinicians make better decisions .
• Customer recommendations: E-commerce platforms like Amazon offer customers recommendations based on their previous purchases or search history. Netflix shows users recommendations based on what they have already watched. YouTube’s recommendation engine draws on past videos and related topics to serve content to its users.
• Fraud detection: Banks and other financial institutions use machine learning to understand patterns and seek out anomalies as possible fraudulent activity.
• Image recognition and classification: Machine learning is helpful for image recognition because it allows companies to build platforms to tag photos, automate cars’ ability to detect possible collisions and even combat unsafe workplace conditions.

4 Types of Machine Learning and How They Function

Experts categorize machine learning processes based on the data they use to train them. The following are four machine learning types:

Supervised Learning

Supervised machine learning uses a complete set of labeled data for training algorithms. A fully labeled data set includes examples with clear expected outcomes. Supervised learning is typically most useful when dealing with classification and regression problems .

A classification problem asks the algorithm to predict discrete values and identify the input data as a specific member of a class or group. If we provide a data set with pre-labeled photos of cats, koalas or turtles, we could judge the algorithm’s ability to classify new images of turtles or koalas.

Regression problems use continuous data, much like those from algebra class. Linear regression, for example, asks an algorithm to determine the value of a variable Y when given the value of variable X.

Example of Supervised Learning

In an example from Nvidia.com , a labeled set of flower images will tell the model which photos are of daffodils, daisies or roses. When shown a new image, the model should compare it to these training examples and (hopefully) predict the correct label.

Unsupervised Learning

Companies often use unsupervised learning when clean and perfect data sets are unavailable. Unsupervised learning also helps researchers ask questions without immediate answers.

Unsupervised learning involves handing the learning model a data set without any explicit instructions. These data sets do not have a specific desired outcome or correct solution. The network attempts to parse out structure through useful features. Depending on the problem, the model organizes the data in a handful of ways:

• Clustering: The most common application of unsupervised learning, clustering seeks out training data that resemble one another and groups them together.
• Anomaly detection: Unsupervised learning can flag any outliers in a data set, like banks looking for unusual spending patterns to detect fraud.
• Association: This unsupervised learning model examines key attributes of data points and tries to predict others based on common association.
• Autoencoders: These models take input data and compress it into code. The model then does its best to re-create the data from that summarized code version. This simple use case has limited functionality, but a complex version can reduce signal noise to improve picture quality.

Examples of Unsupervised Learning

Consider the example of an online shopping cart with diapers, baby formula and burp cloths. In this situation, the website may try to sell you a baby monitor or stroller via association.

In another example , machine learning and clustering can study cancer gene expression and predict cancer at earlier stages.

Semi-Supervised Learning

Semi-supervised learning (SSL) processes to bridge the gap between supervised and unsupervised learning. In this machine learning system, we provide the algorithm with structured and unstructured data. These guide the algorithm toward making independent conclusions. The idea here is that combining two data types allows a machine learning algorithm to learn how to label unlabeled data.?

Semi-supervised learning differs from unsupervised learning because it has several use cases. These include classification, regression, clustering and association.?

A semi-supervised process also has an advantage over supervised machine learning. Because it combines small amounts of labeled data with a large chunk of unlabeled data, it can reduce the expense of manual annotation and save valuable data prep time.

Examples of Semi-Supervised Learning?

One of the most prominent examples of SSL is speech recognition. Because labeling audio takes time and resources, SSL is a way to overcome those challenges while also increasing the ability to understand human speech. Meta (formerly Facebook) applied SSL to speech recognition models .

With just 100 hours of human-annotated data and 500 hours of unlabeled speech data, Meta used the self-training model to increase its performance while reducing word error rates by almost 34 percent.

SSL models constitute a significant part of classifying web content. Google applies SSL to its ranking components to help understand human language and provide users with content that meets their search needs.

Reinforcement Learning

Reinforcement learning (RL) uses a system of “rewards” and “punishments” to offer feedback to the algorithm as it learns from its experiences, usually by trial and error. This method of machine learning most closely imitates the experience of human learning. It can teach machine learning models to follow instructions, operate equipment, conduct tests and more.

Examples of Reinforcement Learning

One of the most prominent examples of reinforcement learning is natural language processing . NLP predicts texts, answers questions and even translates text. RL agents study typical language patterns, then try to emulate and predict how people speak. NLP is a common function of chatbots, which can simulate human conversation and help users with customer service needs.

Digital marketing is a natural ground zero for advancements in reinforcement learning . Real-time bidding platforms and A/B testing are common forms of reinforcement learning. Brands can place advertisements on search engines and other platforms, and the machine learning processes will determine which performs best and display those ads with greater frequency.

Which Type of Machine Learning Is Right for Me?

Deciding on a machine learning algorithm is much more complicated than it sounds. There is no “one-size-fits-all” use case for machine learning. Machine learning is a complex process with many moving parts, but some of the factors to consider include the following items:

• Data knowledge, including structure and complexity
• Accuracy requirements, where different questions need different degrees of accuracy
• Speed, including how long a person has for analysis
• Variables or features to consider when training models for optimal results
• Parameters, or the factors that relate to training time for generating results

Final Thoughts

Machine learning is an exciting field with plenty of implications for businesses worldwide. But before any company dives headfirst into using it, they should understand the differences between types of machine learning processes.

Top Takeaways

• Machine learning is training an AI to solve problems by providing it with data.
• Most machine learning algorithms use labeled, unlabeled or a mixture of both data types.
• While machine learning is a staple of modern tech companies, any industry that relies on data can use it to glean insights.
• Choosing the correct machine learning model depends on factors like required accuracy, data availability and necessary parameters and features.

(Reporting by NPD)