What Are Machine Learning Models? Types and Examples

Modernizing the obsolete methods through which corporations have been gathering data necessitates machine learning (ML) models. They are vital if stakeholders genuinely want to embrace artificial intelligence, chatbots, and codeless human-machine interactions. From recognizing beneficial sales patterns to foreseeing distribution hurdles, leaders want to leverage ML across all operations. After all, ML can learn from all interaction points that a proactive IT ecosystem saves or synthesizes. Later, artificial intelligence (AI) tools will further expand what humans can achieve with ML-enabled experiences. This post will explain what you must know about machine learning models, including their types and examples. 

Machine learning models swiftly interpret enterprise data. For instance, they identify trends and patterns and make risk estimations easier than ever. Otherwise, leaders can automate processes without being restricted to a few programmatic possibilities. For most global organizations, such use cases are attractive. Besides, their understanding of ML has evolved, leading to broader implementation of ML and AI tools beyond the controlled, experimental environments. At the same time, they expect several competitiveness threats if they postpone adopting such tech advancements.  

Understanding Machine Learning Models 

A machine learning model is a flexible computer program that requests data, uses it to identify patterns, and simulates humans’ thinking capabilities to make choices. In other words, rather than being programmed explicitly for each task, all ML models learn from examples and previous interactions with human users. The more examples an ML or AI tool sees, the more it learns and the better it is at making good, reliable, logical guesses. 

Familiar Examples of Machine Learning Models: ML and AI Features Rising in Popularity 

Today, from consumer electronics to corporate IT-enabled capabilities, such models help multiple stakeholders worldwide identify pictures, translate text, get relevant movie watchlists, or spot fraudulent activities during high value financial transactions. 

Your nephews and nieces are likely using AI-ML features in Google Photos to remove unwanted background elements within a few seconds without needing a 3-month-long course in Photoshop or Inkscape. Meanwhile, your coworkers might have used ML-based platforms to enhance a technical proposal for a high-ticket deal. 

Another example is how Google employs machine learning models for its search engine to provide better results. On the other hand, Netflix employs ML models to recommend shows based on your history of watching. Simultaneously, Alibaba employs similar techniques to enhance customer support and optimize supply chains. 

These are merely a few ways machine learning delivers value in world businesses. In fact, neither Amazon nor IBM is interested in missing out on the boom in the ML and AI space. Moreover, their diverse, enterprise-grade applications deliver awe-inspiring results for corporations worldwide. 

Alos read: MLOps – What It is? How to Apply MLOps to Computer Vision? 

Types of Machine Learning Models That Everyone Must Know 

1. Supervised Learning Models 

Supervised learning must be placed among the most frequent forms of machine learning. After all, these models are extensively trained using labeled data. But what does that mean? The input data is often accompanied by the right output. So, the model learns from the correct data-and-output pairs. Later, once it completes the training, it can make practical predictions for new, unseen data. 

Consider how the top names in the banking industry utilize AI and supervised machine learning models. For instance, JPMorgan Chase has noted success in employing supervised learning to identify credit card fraud. First, they train their models on historical transactions that are clearly labeled as fraudulent or genuine. Afterward, the model makes predictions about whether new transaction data patterns hint at the involvement of suspicious parties. 

On the high-tech front, Samsung deploys similar models to enhance the performance of its smart devices in South Korea. The models learn all users’ preferences and adjust settings automatically. 

2. Unsupervised Learning Models 

Unsupervised learning models take things a few steps further because they learn from data without requiring labels. Like a human learner, they discover the patterns in the data independently. Unsupervised machine learning models are wonderful for discovering the underlying patterns. Besides, they can cluster similar items, like a human assistant. This type of ML approach is commonly applied to market segmentation and new customer behavior studies. 

Think of Spotify. How do unsupervised ML models assist such platforms? Spotify uses unsupervised learning to develop customized playlists. It correctly clusters listeners with similar tastes and suggests songs accordingly. L’Oréal, in France, has also employed unsupervised machine learning models to study consumer behavior and tailor beauty product suggestions. Such ML models and related AI tools enable companies to build more personalized, unprecedentedly dynamic, and interactive experiences. 

3. Reinforcement Learning Models 

Reinforcement learning is another significant category without which no stakeholders can make sense of research papers on machine learning models or their industrial applications. A reinforcement learning approach focuses on learning through interactions with the environment. They observe the feedback in the form of rewards or penalties. Later, reinforcement ML models modify their actions to achieve maximum long-term success. 

Reinforcement learning is widely applied in the robotics, gaming, and autonomous vehicles industries.  

Related: The AI Boom is Breathing New Life into Robotics Startups 

As of now, Tesla’s Autopilot system is a remarkable example of the reinforcement learning approach. A Tesla car makes decisions based on real-time input, and the model gets better as it receives more data from real-world driving. Meanwhile, Toyota in Japan is working on reinforcement learning systems to make factory robots more adaptable. These models enable machines to learn complex tasks. 

That is why they excel at reducing the need for human intervention. This progress hints at more humane, less stressful work environments. Therefore, human workers can reduce the risk of accidental death due to factory equipment failure or over-work and embrace more creative challenges for career progression. 

4. Deep Learning Models 

Deep learning is essentially a well-known type of machine learning that utilizes neural networks. The networks are based on the human brain’s architecture. As a result, deep learning models are particularly effective in processing unstructured data. Many image management and cloud storage providers already include deep learning capabilities to make finding images, sound, and text data less of a hassle. In other words, the deep learning ML strategy drives most contemporary AI applications. 

Consider social media and content rights protection platforms. Facebook, or Meta, applies deep learning to identify faces in photographs. Similar social networking sites (SNS) can automatically screen out offensive content or categorize content based on whether it is human-created or machine-generated. 

Baidu in China employs deep learning to drive its speech recognition platforms. These deep learning models enable companies to sort through enormous quantities of data with great accuracy. 

Deep learning also has applications in healthcare. Do you know about IBM Watson? It employs this strategy to read medical records and recommend treatment. 

5. Natural Language Processing (NLP) Models 

Natural language processing is a more like a crucial branch of machine learning that involves language. Many NLP models have the ability to read, write, and translate human language. They are implemented in all modern chatbots, virtual assistants, and multi-lingual content analysis ecosystems. 

For illustration, Amazon Alexa and Apple Siri are well-known NLP-powered tools. They comprehend voice commands and can speak in natural language. Furthermore, Flipkart employs NLP models to enhance search functionality on its online platform in India. They make it simpler for users to find what they are looking for by comprehending natural questions. So, in addition to exact keyword matches, contextual relevance gains adequate attention, boosting consumer satisfaction. 

Read more: The Next Tech Time Warp: How Will Artificial Intelligence Possibly Change the World? 

6. Generative Models 

Generative models are growing in demand and fame since they create new content. Older generative artificial intelligence (or GenAI) had a narrow scope concerning texts and simple graphics. However, newer GenAI solutions can deliver novel texts, images, audio assets, and videos. These models are methodologically trained to learn the underlying data structure. That is how they generate new output that is similar to the original data yet distinguishable as a new asset. 

Today, OpenAI’s generative pre-trained transformer (GPT) models are at the center of content generation and coding assistance. In the United Arab Emirates, organizations like G42 are investing in large-scale generative models for Arabic language applications. These models are transforming industries from marketing to design. Moreover, they enable faster content creation and support human creativity. 

Why Do All Types of Machine Learning Models Matter to Modern Business Development? 

Machine learning models assist all organizations, startups, or established industry giants in making more informed decisions. They minimize mistakes. Many AI and ML tools are vital to saving time spent on repetitive, arduous tasks. They also reveal insights that were otherwise not recognized without multiple human-conducted cross-checks. With suitable models, companies can predict demand and price more optimally. Besides, ML, NLP, GPT, and explainable AI (XAI) ensure users can provide better customer experiences, breaking all language barriers. 

BMW, in Germany, employs machine learning models to track quality in its factories. The ML models identify defects early and minimize waste. At the same time, in Brazil, fintech firms, such as Nubank, use machine learning to evaluate credit risk and tailor financial products. These models deliver value at scale and create new opportunities. 

Acknowledging the Other Side: ML-AI Ethics, Challenges, and Considerations 

Although they have advantages, machine learning models also have disadvantages. They need huge quantities of high-quality data. After all, skewed, biased data will result in unreliable outcomes. Consequently, avoiding bias in training data is also important. Otherwise, those who use an ML output with little to no cross-validation will depend on unfair or incorrect predictions. It would be a decision-making chaos. 

Additionally, firms must keep an eye on data privacy and regulatory requirements. In Europe, many regulations dictate how brands and their data partners can legally use customers’ records or employees’ personal data. Unsurprisingly, ethical AI-ML development and deployment firms such as SAP are creating technology that allows all machine learning models to become more transparent and responsible. Remember, due care in AI use is becoming a worldwide priority. 

Continue reading: The Ethics of AI: Balancing Innovation with Responsibility 

Conclusion: Let’s Look Ahead to an Empowering World of Machine Learning Models 

The future of ML models is both highly exciting and full of complicated multi-stakeholder conflicts. However, it is inevitable that as algorithms become more sophisticated, they will play larger roles in how leaders and their team members make decisions. Undoubtedly, companies that accept this change will be in a better, more rewarding position in the future. 

Today, across the globe, all firms are pushing the boundaries of what you can make possible with different types of machine learning models. Whether it is Microsoft in the U.S., Tencent in China, or startups in Kenya, the momentum is building, and new examples are emerging. 

Machine learning models are not just a trend. Instead, they are now a foundational technology for the digital economy. 

In short, by knowing what kinds of models exist and what they are suitable for, business leaders can make wise decisions. Investing in machine learning is not merely a technology play with a finite goal of getting media buzz going. Instead, it is about creating a more intelligent, more responsive company ecosystem. In a data-driven world, that is an advantage nobody can risk to lose. That also suggests the need for ethical, multidisciplinary, and experienced ML developers will exponentially increase. 

About SG Analytics 

SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. 

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