Machine learning — definition, models, and applications

In a fast-paced and competitive digital environment, with customer expectations higher than ever, businesses need to be smart, move fast, and respond intuitively to stand out. Automating certain tasks and processes can help a company streamline operations and get ahead.

Decision-makers need to understand machine learning and how it works so they can incorporate it into their business and achieve the automation necessary to be successful. In this guide, you’ll gain a deeper knowledge of the core concepts of machine learning and get a better idea of which models can help with your automation and data processing needs.

This article will cover:

• What machine learning is
• Machine learning vs. deep learning vs. neural networks
• How machine learning works
• Machine learning uses
• Machine learning algorithms
• Challenges for machine learning

What is machine learning?

Machine learning is a component of artificial intelligence (AI) that gives machines the ability to learn automatically from past experiences and data while noting patterns to create predictions with little to no human intervention. Using the data it processes, machine learning software imitates how humans learn and becomes more accurate over time.

Machine learning is the technology behind chatbots, language translation apps, show and movie suggestions in streaming services, and the posts that show up on your social media feed. It gives computers the ability to acquire knowledge without specifically being programmed to know certain pieces of information. It can improve personal and professional functions in our everyday lives.

Machine learning offers some significant benefits. It has the ability to take in and process huge amounts of data — far beyond human capabilities — and quickly learn from that information. Machine learning can differentiate objects and recognize faces, giving us the facial recognition technology many people have on their smartphones. It can quickly compare data and provide a variety of options and solutions that would take a human much more time.

Machine learning is also a key component of marketing. For example, it can allow social media platforms to target advertisements to every individual’s feed. Machine learning extends communication abilities and creates more personalized experiences for consumers. Helplines and chatbots are also made possible because of machine learning, and they can assist more customers than if companies only relied on a human workforce.

Machine learning vs. deep learning vs. neural networks

While machine learning and neural networks are often used interchangeably, they are not the same. Neural networks are a subfield of machine learning, and deep learning is a subfield of neural networks.

Artificial neural networks are modeled on the human brain, with thousands or millions of processing nodes that are interconnected and organized into layers. Neural networks comprise three node layers — an input layer, one or more hidden layers, and an output layer.

Each node connects with another and has its own weight and threshold. If the output of a node is higher than its specific threshold value, that node will activate and share data with the next layer in the network. Apart from that, no data passes to the next network layer from that node.

Also modeled on the way the human brain works, deep learning networks are neural networks with many layers. According to the MIT Sloan School of Management, “the layered network can process extensive amounts of data and determine the ‘weight’ of each link in the network.”

Deep learning can use labeled datasets to guide its algorithm, but it doesn’t necessarily need them. Deep learning takes in raw data, such as images or text and automatically recognizes certain features that will separate different sets of data from one another. The need for human involvement is less frequent, and it can handle larger datasets compared to traditional (non-deep) machine learning, which is more dependent on human intervention.

How machine learning works

Machine learning essentially uses algorithms to create more accurate predictions. These algorithms can be:

• Descriptive — using data to interpret what occurred
• Predictive — using data to foresee what will take place
• Prescriptive — using data to suggest actions to take

The algorithms consist of three parts:

1. A decision process. For the most part, machine learning algorithms are used to guess and organize incoming information. Based on the provided data, the algorithm will create a prediction about a pattern within it.
2. An error function. This part of the algorithm assesses the model’s prediction. If there are examples that have already been investigated, an error function can create a comparison to evaluate the accuracy of the model.
3. A model optimization process. If the model can adjust more easily to the data points in the training set, weights will adjust to decrease any discrepancy between the investigated example and the model’s prediction. This process repeats by the algorithm, which updates weights until the threshold of accuracy has been reached.

There are different ways that these algorithms can be taught how to use data. Let’s take a look at the four main approaches to machine learning.

Supervised learning

Using labeled datasets to train algorithms, this subcategory of machine learning follows instructions based on the information it is given.

Machines are taught information from labeled datasets and authorized to guess outputs based on the provided instructions. The labeled dataset identifies input and output parameters that are already depicted, and the machine is taught with the input and corresponding output.

Supervised learning is further divided into two broad categories:

• Classification. These algorithms respond to classification issues where the output component is categorical. Some examples of this are “yes or no” or “true or false.” An example of classification used in daily life is the filtering capabilities in email applications — choosing primary email box messages versus spam box messages. Some recognized classification algorithms include logistic regression algorithm, support vector machine algorithm, and random forest algorithm.
• Regression. These algorithms manage regression issues where input and output components have a linear relationship. They foresee what the continuous output components will be. Examples of this would include a market trend analysis or a weather forecast. Some known regression algorithms include simple linear regression algorithm, lasso regression, and multivariate regression algorithm.

Unsupervised learning

Unsupervised learning is used to analyze and cluster unlabeled datasets to find patterns without the need for human intervention.

An unsupervised machine learning program will search for unlabeled data and find patterns that people aren’t looking for specifically. For example, an unsupervised machine learning program can identify primary client bases for an online store. Some known unsupervised learning approaches include nearest neighbor mapping and self-organizing maps.

The advantage of unsupervised learning is its ability to find similarities and differences between groupings without human intervention. This algorithm can group unsorted datasets by patterns, differences, and similarities.

Unsupervised learning has a couple of sub-classifications as well:

• Clustering. This approach groups objects into clusters based on guidelines such as differences or similarities between them. An example of this is organizing customers by the items they purchase.
• Association. This technique identifies standard relations between the variables in a large dataset. It decides the dependency of data items — and charts the associated variables.

Semi-supervised learning

As its name suggests, this approach merges aspects of supervised and unsupervised machine learning.

Semi-supervised learning reads labeled and unlabeled datasets to teach its algorithms. Combining both of these datasets eliminates the problems that come with using each of them on its own. In addition, the semi-supervised learning approach uses smaller labeled datasets to guide and manage larger unlabeled datasets. The datasets are usually grouped this way because unlabeled data requires less effort and is less expensive to acquire.

Think about a student learning from a teacher. If a student receives information from a teacher, that would be considered supervised learning. When studying independently at home, the student is learning the information without supervision from the teacher. But if the student reviews the information with a teacher in class after learning it, this would be analogous to semi-supervised learning.

An example of semi-supervised machine learning in daily life would be a webcam that identifies faces.

Reinforcement learning

Reinforcement learning trains through reward systems. It uses trial and error to learn as it goes, with successful outcomes reinforcing recommendations. Reinforcement learning doesn’t have labeled data like the supervised learning technique. This type of machine learning works on a feedback process of actions and learns through experiences.

Reinforcement learning takes the most appropriate action by learning from experiences and adjusting its actions accordingly. There are rewards for correct actions taken and penalties for wrong ones. This helps the system learn the correct actions to take.

Reinforcement learning is popular in video games, robotics, and navigation. In video games, for example, the game determines the environment, and every movement of the reinforcement agent will establish its state. The agent will receive feedback through rewards and penalizations, which affect the game score.

There are two types of reinforcement learning algorithms:

• Positive reinforcement learning. This type of reinforcement learning involves the addition of a reinforcing aspect after a certain behavior is performed by the agent, making it more likely that the behavior will occur again in the future.
• Negative reinforcement learning. This type of reinforcement involves the removal of a negative condition to increase the chances of a particular behavior occurring again, or the strengthening of a particular behavior that will avoid a negative result.

Machine learning uses

Many industries that work with large volumes of data see the value in using machine learning technology to boost productivity. Machine learning is not a replacement for humans but rather a tool that helps to extract information quickly and accurately so that humans can evaluate the recommended actions and make better, faster decisions.

Let’s take a look at some of the industries that most commonly use machine learning.

Healthcare

Machine learning is quickly expanding across the healthcare field. Wearable sensors and devices like smart watches or fitness trackers can help medical experts gain real-time insight into a patient’s health. A few benefits of machine learning in healthcare include:

• Analyzing data more quickly and efficiently. With the data presented and analyzed in real time, health red flags or patterns can be spotted easily to provide updated diagnoses or treatments more quickly.
• Assessing patient health in real time for more personalized care. While drugs can treat symptoms, individual patients’ side effects may be different. Machine learning can study an individual’s genes to provide personalized care and targeted treatment for each patient.
• Faster drug discovery. Machine learning can speed up the long and expensive process of creating a new drug. Some machine learning tools can analyze large datasets to help discover new potential drug treatment options.

Finance

Banks and other financial institutions handle large amounts of sensitive information. Many companies have chosen to use machine learning technologies to provide a more secure and efficient service for their customers. Among the benefits of machine learning in finance are:

• New insights into data. New investment opportunities can be discovered quickly, and better insights can be provided to investors — for example, knowing the right time to trade.
• Better fraud protection. Security is crucial when managing financial information. Data mining spots users with high-risk profiles and helps cyber surveillance systems identify potentially fraudulent activities.

Retail

The retail industry uses machine learning technology to create different experiences for each individual and provide additional customer assistance. Machine learning offers retailers the potential to expand their client base while cutting costs. A couple of key benefits are:

• Personalized shopping experiences. Many online retail sites use machine learning to offer suggestions for products based on recent purchases or bookmarks. Chatbots on a retail website can help answer a customer’s immediate questions, freeing up human representatives.
• Improved marketing. Machine learning can be a helpful tool for planning customer merchandise, putting together advertising campaigns, optimizing prices, and providing customer insights.

Machine learning algorithms

Machine learning can be used to create recommendation engines and algorithms to personalize products and services for individuals.

Companies like YouTube and Netflix depend on algorithms to recommend movies and shows to viewers based on their watch history. Products and services can be suggested on retail and other websites — based on saved or purchased items. In addition, social media platforms use machine learning to make recommendations, with different posts showing up on each person’s feed based on posts they have liked or accounts they follow.

Personalizing user experiences and gaining additional insights help organizations to better assist their customers. However, machine learning technology comes with its own challenges too.

Challenges for machine learning

While machine learning has increased efficiency for many businesses in many industries, just like any other new technology, it has some drawbacks. In particular, there are ethical and cost concerns surrounding machine learning technology.

1. Bias and discrimination

Unfortunately, the data used to train machine learning processes has the potential to reflect human bias. Algorithms that learn from datasets that have errors or exclude certain populations create inaccurate representations of the world. These errors fail to capture an accurate model of the world and can also be discriminatory. While most companies do their due diligence to eliminate potential bias in automation efforts, some consequences could emerge from using artificial intelligence.

For example, Amazon used automation to simplify hiring and unintentionally discriminated against candidates for technical positions based on gender. The company then did away with the process. Seeking input and data from people of different backgrounds can reduce bias and discrimination.

2. Privacy

Machine learning requires data, and with that comes concerns around privacy. When managing all types of data — especially personally identifiable information (PII) — data privacy and security are of utmost importance. More and more lawmakers around the world are taking action to protect personal data.

GDPR legislation was established in 2016 to safeguard the personal data of people in the European Union and European Economic Area to ensure individuals had more control over their information. In the United States, California passed the California Consumer Privacy Act (CCPA) in 2018 to mandate that businesses notify consumers when their data is collected.

3. Cost

Implementing machine learning technology into business functions can be costly. Data scientists — the people usually driving these projects — often require high salaries. And the software infrastructure that comes with establishing machine learning practices can also be expensive.

Machine learning is implemented to search through large datasets created over time, and many resources are required to make the technology a useful part of business strategy. The time and resources expended are well worth the benefits for many businesses, but it’s important to remember that machine learning is an investment — and the system can become more complex and costly as it grows.

4. Technological singularity

Technological singularity is also referred to as superintelligence or strong AI. While many people have concerns about AI exceeding human capabilities, experts are not worried about it happening any time in the near future.

While it may not surpass human knowledge at the moment, there are ethical questions about responsibility with AI technology. For example, when it comes to self-driving cars, who is liable in a car accident? Should this type of technology continue to expand in motor vehicles or be limited to promote safer practices? As AI technology continues to grow, these kinds of questions will need to be addressed.

Evaluate how machine learning can help your business

Machine learning has the potential to dramatically boost business productivity as it can process a lot more data at a much faster speed than humans can. When you’re ready to get started, evaluate which tasks and processes at your company could benefit from machine learning automation.

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