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What Is Machine Learning?

By Steve Smith

The information presented here is true and accurate as of the date of publication. DeVry’s programmatic offerings and their accreditations are subject to change. Please refer to the current academic catalog for details.

 

March 22, 2024

5 min read

 

As technology continues its leap forward, machine learning is a term you may be hearing more and more often. But what is machine learning? 

 

Machine learning is a subfield of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. It is also an important component of data science. According to leaders in the field, many of the advances in AI during recent years have involved machine learning in some way.

 

According to researchers at MIT, the function of a machine learning system can be:

 

  • Descriptive: The system uses the data to explain what is currently happening

  • Predictive: The system uses the data to predict what can happen in the future

  • Prescriptive: The system will use the data to suggest a course of action to solve a problem
     

Existing at the confluence of data science and engineering technology, machine learning is the technology behind the chatbots that assist you in online customer service experiences, the shows and movies your streaming services recommend, your social media feeds and self-driving cars. Machine learning is also being put to use in the medical field, with machines that can diagnose medical conditions by analyzing MRI images.

 

To answer “what is machine learning” accurately, we must consider the emergence of big data and its implications. The term big data refers to data that contains the 3 V’s: greater variety, arrives in increasing volumes and with more velocity. With the development of Internet of Things (IoT) technologies, connected devices are giving companies more ways to collect massive amounts of consumer data for the decision-making advantages it can deliver. When we talk about massive data sets, we’re describing amounts of information that are so large and complex that data scientists could not manage them using traditional data processing software. 

 

As you might speculate, large enterprises are expected to increase their tech spending to deploy AI and machine learning technologies, and the small and mid-sized segment is expected to follow suit, propelling the global machine learning market from a 2023 value of $26.03 billion to $225.91 billion by 2030. 

Types of Machine Learning

There are several types of machine learning models, each with attributes and algorithms that make them useful in data analysis, outcome prediction, image and pattern recognition and other applications.

What is a machine learning model? Here’s a breakdown:
 

  • Supervised learning: Used to train algorithms to classify data or accurately predict outcomes, supervised learning uses labeled data sets to help organizations solve a variety of real-world problems at scale. As data is fed into this model, it adjusts its weights until it has been fitted appropriately.

  • Reinforcement learning: Rather than being trained using simple data, this model learns by trial and error. When it identifies a sequence of successful outcomes, it reinforces them to develop the best recommendation or policy for a given problem. 

  • Unsupervised learning: In unsupervised machine learning, algorithms are used to analyze and cluster unlabeled datasets, discovering hidden patterns or data groupings without the need for human intervention. This method is helpful for exploratory data analysis, cross-selling strategies, customer segmentation and image and pattern recognition due to its ability to discover similarities in information. 

  • Semi-supervised learning: This middle ground between supervised and unsupervised machine learning can solve the problem of having insufficient data for a supervised learning algorithm, or when it is too expensive to label enough data for a supervised learning algorithm.
     

When these models need to be calibrated or adjusted, regularization comes into play. What is regularization in machine learning? It’s a technique used to fine tune a learning model to reduce errors and avoiding overfitting, or when a model performs accurately using a training set but doesn’t perform well with new data.

Machine Learning Algorithms

Without machine learning algorithms, the machine learning models we just described couldn’t function. Let’s take a close look at 5 algorithms that are at the core of machine learning models:

  • Decision tree: This supervised learning algorithm is one of the most popular in use today and is used for classifying problems. Decision trees are basically diagrammatic, logical approaches to problem-solving, which divide populations into two or more homogeneous sets based on the most significant attributes or variables.

  • Random forest: The random forest model is a collective of decision trees. Each tree in the forest is classified to classify a new object based on its attributes, and the tree votes for that class. The forest chooses the classification that has the most votes.

  • Naive Bayes: This easy-to-build model is useful for massive sets of data and known for its simplicity. The Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Even if identified features are related to each other, the model considers all properties independently when calculating the probability of a particular outcome.

  • Linear regression: Linear regression is a process where a relationship is established between variables by fitting them to a line. This algorithm can be used to track things like sales numbers, score predications, economic growth and more.

  • K-means clustering: This unsupervised learning algorithm is intended to solve clustering problems. Data sets are classified into a particular number of clusters in such a way that all the data points within a cluster are either like or unlike the data in others.

AI and Machine Learning

Now that we’ve identified the different methods of machine learning and some of its commonly used algorithms, let’s take a look at the relationship between AI and machine learning. As we covered earlier, machine learning is a type of artificial intelligence and plays an important role in the AI ecosystem. As we dive deeper into “what is machine learning”, let’s take a look at the different ways businesses are using it:

  • Speech recognition: Also known as automatic speech recognition (ASR), this capability uses natural language processing to translate human speech into text. It’s likely that you’ve used this technology when searching for information using voice searches, interacting with virtual assistants or using speech-to-text.

  • Chatbots: Online chatbots are utilized in the customer service area and changing the way we engage with businesses. Chatbots have been useful in answering customers’ frequently asked questions, suggesting products or cross-selling.

  • Computer vision: This machine learning technology enables computers to assimilate information from images or videos and then take appropriate actions. Powered by neural networks, this technology has applications in photo tagging on social media, self-driving cars and medical imaging.

  • Recommendation engines: This widely used machine learning application uses consumers’ past behavior to make product recommendations that will interest them in the hopes that they’ll make a purchase. When you receive an email from your favorite online retailer with a personalized message or scan the home page of your favorite streaming service and see suggested TV shows and movies, you’re seeing recommendation engines in action.

  • Automated stock trading: High-frequency trading platforms use machine learning to make stock trades each day without human intervention.

  • Robotics: Machine learning is powering trends in automation and technology. Robots and remote operations are enabling manufacturers to reduce their workforces while increasing production output. Using robotic process automation (RPA), businesses can automate repetitive tasks such as capturing information from customer invoices or service requests.

Advantages of Machine Learning

Machine learning has the potential to deliver significant user benefits across consumer and industrial markets and applications, increasing efficiency and accuracy, enhancing security and reducing risk. 

While you may be thinking machine learning is a technology that can only be fully embraced by industrial and retail giants, small to medium-sized businesses can also reap significant benefits by leveraging the latest machine learning tools. Machine learning can provide business owners with accurate and timely dashboards that show their company’s financial health, while their marketing teams can use it to gain a better understanding of their audiences with real-time market insights, helping them create better online experiences, improve performance and cultivate trust in their brands.

Other ways machine learning can help businesses thrive include:
 

  • Streamlined processes: With massive amounts of data and machine learning algorithms, businesses can find ways to improve workflows and eliminate ineffective practices. This efficiency may result in improved agility, productivity, and reduced waste.

  • Cyber security tracking: Machine learning-based software can help to detect theft, identify a security lapse and perform tasks such as profiling potential customers or identifying cases of fraud. Machine learning can be used to defend against phishing attacks by monitoring all incoming messages, looking for suspicious signs in content, links, IP addresses, then identify and quarantine or destroy possible threats.

  • Customer experience improvement: Customer service and retention are areas where machine learning can be really impactful. Machine learning technology can help small businesses provide interactive customer service that uses AI to give answers to questions and direct customers to the right department to manage their concerns or collect feedback. It can also enable personalized recommendations that can help nurture customer engagement.

Planning to Pursue a Tech Career? Begin Your Journey at DeVry

Fueled by trending technologies like AI and machine learning, the field of engineering technology presents opportunities for those with natural curiosity, problem-solving strengths and desire to pursue a career in tech. 

DeVry University has a long history in the Engineering Technology field. Our first Associate in Electronics Engineering Technology program was accredited by the Engineering Technology Accreditation Commission of ABET (ETAC of ABET) in 1955, and our first Bachelor’s in Electronics Engineering Technology was also accredited by ETAC of ABET in 1970. Today, our Engineering Technology programs continue this strong tradition of ABET accreditation with a curriculum designed to meet the needs of 21st century engineering technologists. 

At DeVry, our Bachelor’s Degree in Engineering Technology program can help you build skills in technical operations, systems and processes. Coursework in machine learning, project management, circuit analysis, automation control and other disciplines will help you gain career-ready skills that employers may be looking for. And to help you build on your skills along the way, our Undergraduate Certificate in Engineering Technology and our Associate Degree in Engineering Technology stack directly into our Bachelor’s Degree in Engineering Technology,1 allowing you to continue your education with us and apply qualifying credits toward your next degree level. 

DeVry's Associate Degree in Engineering Technology and Bachelor’s Degree in Engineering Technology are accredited by ETAC of ABET. This is a global mark of quality that is respected by employers and professional associations within the Engineering Technology field.
 

 

1At the time of application to the next credential level, an evaluation of qualifying transfer credit will occur and the most beneficial outcome will be applied. Future programmatic changes could impact the application of credits to a future program. Refer to the academic catalog for details.

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