What is machine learning?

Machine learning explained
Why is machine learning important?
How does machine learning work?
Machine learning phases
Advantages of machine learning
Challenges of machine learning
Types of machine learning algorithms
What is the difference between machine learning and AI and deep learning?
How different industries are using machine learning

Key takeaways:

Machine learning or ML is a branch of artificial intelligence where computer systems learn to recognize patterns, perform tasks, and make decisions based on data without being specifically programmed to do so. Machine learning models are trained by machine learning algorithms on substantial datasets to automatically and continually improve their performance.

Leveraging generative AI requires embracing the idea of "human first and human last" because human oversight is necessary at every stage, from prompting to validating the output.

Machine learning explained

Machine learning (ML) is an integral part of computer science and technology. Machine learning’s definition is a subset of artificial intelligence (AI) designed to imitate how people learn and gradually improve. As concepts, machine learning is sometimes synonymous with computer programs that learn through utilizing machine learning algorithms (MLAs) to become more accurate by receiving more data. It’s not hard to compare machine learning to cooking: the MLA is the recipe, the data is the ingredients, and the machine learning model (MLM) is the cooked meal.

Machine learning algorithms are sets of rules or procedures to help the machine learning models process data, discern patterns, and then reapply this knowledge using new data to generate insights or perform a task.

Machine learning models are the product or output of MLAs post-dataset training. MLMs are computer systems that function like a program that learns to recognize patterns and make predictions on the initial data or on new data sets without an end user explicitly programming the machine learning models for each task. A MLM usually can generate predictions with a certain level of confidence and precision that corresponds with the level of training that the algorithm received. MLMs can continually improve, becoming progressively better with the inclusion of additional data and experience.

Why is machine learning important?

Machine learning is an integral technology in today’s global economy and is often a significant competitive differentiator. Machine learning models help enterprises propel innovation for developing new products and services and offering insight into emerging trends, such as customer behavior and business patterns.

There are many real-world applications for machine learning tools and AI-powered devices in our daily lives. They include phishing and spam filters for our email applications and websites, banking algorithms that look for fraudulent transactions, and recommendation models and algorithms found on everything from audio and video streaming platforms to search engines.

How does machine learning work?

Machine learning tries to emulate how people learn and apply information by programming machines to imitate cognitive functions that people naturally possess, such as problem-solving.

But how can a machine go through the same learning process as the human mind:

Receiving information
Analyzing and interpreting information
Verifying the interpretation is correct
Refining the interpretation
Applying the knowledge gained from the information

The answer is to train the machine to make a predictive model that functions as the lens for the machine to analyze its data and learn. End users can begin this process by providing data to a computer system and prompting it to choose a learning model to teach the system how to process the data.

Neural networks

Similar to how machine learning is a subset of AI, neural networks are a subset of machine learning. Also known as artificial neural networks (ANN) or simulated neural networks (SNN), neural networks get their name from how they mimic the human brain and how the brain’s neurons signal to each other.

Machine learning phases

Most machine learning systems operate on the following phases:

Collecting and processing data: The foundation of machine learning, data is first collected and then prepared, then sorted into training data and testing data. Any unwanted data is removed. After the data is organized, it can be easily accessed and interpreted.
Deciding on a model and algorithm: Machine learning algorithms are run on data that support machine learning models for specific task tracking. Different MLMs and MLAs are selected depending on the task or problem that needs to be solved.
Training the model: The machine learning algorithm receives preprocessed data and processes the data, searching for patterns and relationships. Different objective functions and techniques within the machine learning model help with this phase. The loss function looks for errors during training and the optimization technique strives to minimize training errors by adjusting the machine learning model’s parameters. This process is then repeated, allowing the model to refine and improve its performance.
Evaluating the model’s performance: Once the machine learning model has been sufficiently trained, then the MLM is evaluated. The model receives previously unseen data and is tested with metrics that calculate the precision of the model’s predictions and its performance for delivering the desired results for the task or problem that needs to be solved.
Fine-tuning the model: The machine learning model’s evaluation displays if it’s performing as desired or needs improvement and can spotlight if the MLM has any weaknesses. If there are weaknesses, then additional training with specific data can supplement the model’s general data training to increase the model’s accuracy or performance for delivering a desired result or solving a particular task or problem. This process can be repeated until the model’s results are satisfactory.
Deploying the model: When the machine learning model is optimized and is likely ready to be deployed and make new predictions based on real-world scenarios. The MLM is then integrated into an environment and put to work. The model’s performance is monitored, and its data is collected and evaluated for the overall correctness of its predictions. Post-deployment, the model will continue to be refined, addressing any issues that arise and help to improve the model’s results.

Advantages of machine learning

Modern enterprises rely on data to help them make their critical decisions. Machine learning projects have created new possibilities and changed how data is collected, organized, analyzed, and leveraged. Organizations across industries can use machine learning algorithms to help machine learning models ingest and learn from data, then make predictions and decisions based on quickly evolving situations and without needing end users to program them.

Here are several of the advantages that come with leveraging machine learning:

Quickly handling large amounts of data
The ability to process and analyze vast volumes of complex data with speed and precision is one of the largest and most common advantages of applied machine learning. End users struggle when tasked with manually assessing and processing large datasets. When presented with the same task, machine learning models can quickly and accurately identify unseen patterns and trends in real time.

Machine learning’s speedy data analysis helps organizations to discover valuable insights and reveal hidden patterns and trends. This benefit helps organizations to make data-driven decisions and to flexibly and quickly respond to changing situations. It can also help organizations to optimize their operations and mitigate their risks.

Improves efficiency and automation
Machine learning is at the root of many of the technologies that make workers more efficient. It helps with automating many low-cognition repetitive tasks, including spell-checking, document digitization, and classification

Machine learning systems help enterprises to continuously improve and automate routine and tedious tasks. Many machine learning models can perform low-demand, time-consuming tasks, including scanning and reading documents, summarizing reports, transcribing dictation and similar audio, and tagging content.

By automating these repetitive tasks that end users would otherwise be doing, machine learning models can complete the tasks more quickly and accurately, improving an organization’s operational efficiency and potentially increasing its profitability. Automating these tasks also frees up the end users, allowing them to focus on tasks that can’t be automated, such as tasks that require more creative problem-solving.

Creates personalized experiences
Machine learning helps create unique and personalized customer experiences by leveraging behavior patterns and preferences. Customer data, such as their purchase history, internet browsing history and search history help enterprises to curate and create customized recommendations for their customers. Streaming services can create viewing recommendations and retailers can recommend different products. Machine learning systems can analyze customer data and then produce recommendations for targeted products and content based on a customer’s past behavior patterns.

Machine learning systems can create individual customer profiles and later cross-reference these profiles against other similar profiles to make predictions about customer interests. Suggestion engines will leverage personalized engagement offers, such as coupons, discounts, or targeted ads to keep customers engaged.

Cybersecurity and fraud detection
Machine learning systems help enterprises maintain a security-rich environment and prevent or otherwise minimize fraud. Because of how fast machine learning models can analyze data, they are also used to quickly analyze emails and determine if they are safe or unsafe, such as a phishing email or an email with malware as an attachment.

Many of today’s financial organizations leverage machine learning algorithms for advanced fraud detection. These MLAs work in real-time to analyze transactional patterns and target suspicious activity. Machine learning security systems help keep networks safe from cyberattacks, ransomware, malware, and similar cyber threats by monitoring and analyzing in-network traffic patterns and isolating any possible threats.

Forecasting trends
Machine learning models and algorithms are frequently leveraged to recognize patterns and make predictions based-off insights learned from data. Similarly, machine learning systems can be leveraged to help enterprises forecast future trends with historical data analysis.

Machine learning systems drive predictive analytics, an advanced form of data analytics that strives to answer to the question of “what’s next?” Predictive analytics helps enterprises to make predictions about future events and trends, which also helps enterprises with making informed decisions, optimizing operations, and risk mitigation.

Predictive analytics can help organizations anticipate industry needs by forecasting sales and demands and help prevent equipment failure by anticipating when parts will need to be repaired or replaced.

Challenges of machine learning

Despite all the advantages they bring, machine learning projects are only as effective as the machine learning systems and resources built around them. Although the appeal of leveraging machine learning systems is that they can be semi-autonomous and function without intervention from end users, they also have challenges and require some planning and preparation for them to be able to successfully perform their task or solve their problem.

Here are several examples of the challenges that come with leveraging machine learning:

Data quality
Data is a vital part of what machine learning is and what machine learning does. The quality of data that a machine learning model is trained on has a stark impact on how the MLM will perform. Put generally, high fidelity data helps MLMs produce more accurate and efficient results and low fidelity data that contains errors or inaccuracies can result in a machine learning model producing inaccurate and inefficient results or otherwise cause issues with the MLM’s performance.

IT teams must be diligent about checking their data sources, performing cleaning procedures, training end users on processes and protocols, and integrating tools to assess data quality and fit to help ensure that the data quality is high before the data is ingested by the machine learning model.

Data bias
Creating the optimal data for your machine learning model is a challenging part of creating machine learning tools. Locating specific types of data can cause an imbalance in the machine learning model that leads to bias.

For example, if an end user is training a machine learning model to identify paint, then the end user must include data about as many colors and types of paint as possible. If they were to only include one color and one type of paint in the MLM’s training, then it might create unintended bias and produce inaccurate and poor outcomes in the paint-identifying task.

The end user can leverage data resampling techniques, along with anomaly detection algorithms, and a variety of evaluation metrics to help mitigate bias in the MLM.

Data privacy
The ability to quickly process and analyze vast volumes of complex data is one of machine learning’s advantages. However, the fact that machine learning models frequently rely on massive amounts of sensitive data can be challenging because of how the sensitive data could be vulnerable to data breaches, misuse, and similarly dubious acts, issues, and threats that could compromise the data through the ML system.

The lack of transparency in complex MLMs, sometimes referred to as machine learning’s black box problem or black box nature, makes it difficult to determine exactly how the data is used and evaluate for privacy risks. This lack of clarity is particularly obvious for MLMs that use deep learning and leverage non-linear multifaceted interactions across a seemingly incalculable spread of parameters within deep neural networks. This vast complexity renders the process of tracing how inputs are turned into outputs incredibly challenging.

Enterprises leveraging machine learning while striving to enforce data privacy also must grapple with machine learning compliance and regulatory complexity. The enterprise must also ensure that the users who supplied the data have endorsed their data being used with their complete informed consent.

Data security
Data security is first considered a benefit of machine learning, such as how ML systems help enterprises maintain security-rich environments and minimize fraud. However, machine learning can also introduce security issues and vulnerabilities. When a machine learning model is analyzing data, the MLM probably is not checking to verify if the data contains private or similarly sensitive information.

Data can also be the target of cyberattacks, including data poisoning attacks attempting to corrupt training data and create misinformation and adversarial attacks attempting to trick the MLM with manipulated inputs.

Enterprises can help minimize data-based security vulnerabilities by taking data preparation steps and having and enforcing data security policies, procedures, and controls.

Types of machine learning algorithms

Different MLAs are usually applied depending on what their end user’s goals are. Other factors include how the algorithms are fed data and how their end user wants the algorithms to learn or be trained.

Machine learning algorithms include the following types:

Reinforcement learning: Leverages trial and error learning to reach their results according to pre-established actions, boundaries, and end values.
Semi-supervised learning: Leverages labeled and unlabeled data and often learns to label the unlabeled data.
Supervised learning: Receives datasets along with desired inputs and outputs. Data scientists actively tweak this algorithm, improving its accuracy until it reaches a desired level.
Unsupervised learning: Unlike supervised learning algorithms, this algorithm receives no correction or interaction from operators and interprets and organizes data without pre-established actions, boundaries, and end values.

What is the difference between machine learning and AI and deep learning?

The terms “machine learning” and “artificial intelligence” or “AI” are sometimes confused with one another or used synonymously, but they are not the same thing. Machine learning and deep learning (DL) are both subsets of AI, and while all ML is AI, not all AI can be considered machine learning. In DL, artificial neural networks that mimic the human brain are used to perform more complex reasoning tasks without human intervention.

AI can be a sort of catch-all term for the different things that make robots similar to humans. Examples of AI include digital assistants such as Amazon’s Alexa and Apple’s Siri and the face-unlock technology on most smartphones.

AI is an umbrella term for different strategies and techniques used to make machines more human-like. AI includes everything from smart assistants like Alexa, chatbots, search engines with AI-powered algorithms, map apps such as Waze and Google Maps, and content generators such as Microsoft Copilot.

Other examples of common fields within the broader field of AI include the following:

Generative AI (GenAI): A subset of deep learning, GenAI is used to create new content, often text, images, audio, or code. GenAI is often powered by large language models (LLMs) trained on vast data sets to quickly produce user prompt-based outputs.
Natural language processing (NLP): A subset of computer science, AI, linguistics, and ML focused on creating software capable of interpreting human communication.
Robotics: A subset of AI, computer science, and electrical engineering, robotics is based on developing robots capable of learning and performing complex tasks in real-world environments.

How different industries are using machine learning

Machine learning is an integral tool for today’s global industries because of its myriad applications and capability for driving efficiency and competitive advantage.

Here is how machine learning is playing a part in several major industries:

Banking and finance
Fraud prevention and risk management are two areas where machine learning has been an asset for the banking and finance industries. ML also helps with assessing credit risk, giving personalized financial advice, forecasting trends and generating insights to help with making optimal trading decisions.

Customer service
The customer service strives to help customers attain whatever they are seeking as quickly and efficiently as possible. The industry leverages intelligent chatbots to help with proactively identifying and resolving customer requests and issues. Machine learning processes and analyzes customer feedback, often looking for emerging trends and issues.

Healthcare
The healthcare industry strives to quickly and efficiently treat the patients who rely on it. The industry leverages machine learning to provide improved patient care and deliver real-time patient monitoring and improve patient diagnostics. Machine learning models can scan x-rays for broken bones, tumors, and similar abnormalities and AI applications can supply hospitals with personalized treatment programs and resource allocation systems.

Retail
Machine learning continues to have a large impact on the retail industry. ML helps retail enterprises create improved, personalized customer experiences by leveraging product recommendations, targeting promotions, and AI-powered assistants. It also helps retail enterprises to optimize their operations and supply chain with tools for demand forecasting, inventory management, and supply chain optimization.

Travel and transportation
One of the most obvious ways that the travel and transportation industry leverages machine learning is through safety by detecting traffic hazards and anomalies and striving to prevent potential accidents from occurring. ML also helps provide public transit users with predictive scheduling, reduced wait times, and an overall improved rider experience. For commercial and freight transportation, machine learning supports route optimization by analyzing real-time traffic and weather to provide the optimal route for saving time, reducing emissions, and minimizing costs.

FAQs

AIOps integration platforms can improve IT operations by using classical AI, generative AI, and machine learning to provide better performance and maximize investments.

Machine learning can help with meeting customer demands, including benefits like helping create personalized customer experiences and predicting future customer behaviors. Kyndryl Collaborative leverages machine learning with AI and insights from analytics to meet our customers’ needs.

Machine learning, deep learning, and AI are tools that can analyze vast amounts of data, then locate patterns within that data and determine when these patterns might repeat in the future. AI and machine learning can model alternate configurations, boosting uptime and resiliency, locating opportunities for preventative maintenance, and targeting potential cybersecurity risks.