Machine learning (ML) and artificial intelligence (AI) are two common terms in almost every boardroom. From advanced customer analytics and streamlined inventory management to rapid document processing and industrial automation, the current state of technologies holds massive potential for almost every industry.
According to a 2023 study by Microsoft, 71% of businesses already use AI in some capacity, and another 22% plan to do so within the next 12 months. Among the adopters, every $1 invested in AI now brings an average return of $3.5 or a ROI of 250%.
Undeniably, the potential for value generation is massive, but algorithm development is a complex process requiring a clear vision, a sharp business case, and teams with the right skill sets.
If you’re sizing up the potential of AI, this post explains what machine learning is in a nutshell. We’ll explore three common types of machine learning methods, along with their advantages and best-suited business use cases.
What is Machine Learning (ML)?
Machine learning (ML) is a scientific approach to predicting values from data using statistical algorithms. Unlike traditional programming, where applications are explicitly programmed to perform certain tasks, machine learning systems can independently draw correlations from data to identify different relationships and make predictions. ML systems also progressively improve their performance as they get exposed to more data.
Machine learning in a nutshell is a method for creating systems that learn from and make predictions or decisions based on data.
Classical Machine Learning vs Deep Learning vs AI
Machine learning (ML), deep learning (DL) and artificial intelligence (AI) are nested concepts.
Source: PwC
- AI is a broader research field, covering different approaches and algorithm types that aim to mimic human intelligence (including deep learning, robotics, and natural language processing among others).
- ML is a subset of methods for training machines to extract insights from available data and learn from it under human supervision.
- DL is a subfield of machine learning, focused on developing algorithms that require less human intervention and supervision.
- Gen AI is a subclass of algorithms, developed specifically for generating new content, based on provided user prompts. These tools are easily accessible both to line-of-business users and developers, looking for ways to incorporate these models into their products.
Classical machine learning algorithms rely on labeled datasets for training, “Deep” machine learning algorithms, in contrast, can be trained on raw, unstructured data (e.g., audio inputs or visuals) and can self-determine the important data features to properly categorize the input.
DL allows the use of larger data sets and eliminates some human intervention, required for training algorithms. As MIT professor, Lex Fridman, sums it up: “Deep learning is scalable machine learning”.
Source: MIT
In short, artificial intelligence is the umbrella term, covering all approaches and technologies for teaching machines to mimic human intelligence.
Machine learning and deep learning sit under this umbrella. The two subfields rely on different approaches for training machines to perform “intelligent” tasks.
Types of Machine Learning Approaches
Traditionally, machine learning is divided into supervised, unsupervised, and reinforcement. Each approach works best for certain use cases and scenarios.
Supervised machine learning models are trained with labeled data sets, for example — a set of dog pictures. By analyzing human annotations about different data features (e.g., fur color, ear shape, etc), supervised algorithms will learn to distinguish between a cat and a dog.
Unsupervised machine learning algorithms scan for patterns in unlabeled data without any explicit prompts or commands for what to look for. For example, an unsupervised machine learning algorithm may analyze sales datasets to identify best-selling product categories, using features the system considers important.
Reinforcement machine learning algorithms are asked to complete the task and receive positive or negative feedback, based on the outcomes. For example, a reinforcement learning system, piloting a self-driving car, receives positive feedback on timely braking. Over time, it learns when to best break to achieve a bigger reward.
Source: MIT Sloan
Let’s further analyze how each works and in what use cases they support.
Supervised Machine Learning
The basic idea of supervised ML is that an AI developer specifies a set of input parameters and a result you expect to get. Along with the input parameters, the data should contain answers, aka “labels”. For example, to forecast the exchange rate, the exchange rate value will serve as a shortcut. The algorithm then relies on labels and different learning techniques to come up with the correct answers.
After learning the task using a training data set, a supervised ML model can effectively process new similar data (aka such it hasn’t been seen before) to provide accurate classifications and predictions.
Source: Nvidia
The advantages of supervised machine learning are:
- Versatility due to the wide availability of different, well-tested supervised learning algorithms, suitable for different types of analytical problems.
- Ease of training compared to other machine learning techniques with fewer computing resources required.
- High model explainability, which promotes easier audits and compliance, plus increases trust from users.
- Solid optimization potential. Using labeled training data, the algorithm identifies the most relevant features and relationships, thus improving performance.
On the downside, supervised machine learning is heavily reliant on data availability and solid data management practices. In fact, 28% of ML teams blame “lack of access to quality data” as the main reason for failed or postponed ML model deployments.
Likewise, supervised machine learning models require a clear research question (e.g., predicting customer churn) and supporting data points to explore it (e.g., the number of canceled accounts in 2022). Such models cannot be used for explorative, ad hoc data analysis, where you’re trying to locate potential correlations and patterns in a large data set (e.g., identify the reasons behind customer churn).
Types of Supervised Learning
- Classification algorithms help identify input data as members of a particular class. For example, the algorithm receives data points, associated with fraudulent payment transactions for training and a dataset for categorizing new transactions as fraudulent or regular. The model is then evaluated on how accurately it can classify new transactions.
- Regression models make predictions about specific numbers by analyzing the relationships between data inputs and target variables. For example, by analyzing data points such as “house size”, “zip code”, and “historical pricing trends”, the algorithm can predict future house prices under different market conditions. Unlike classification models, which organize data into categories, regression models can produce statistical predictions.
- Naive Bayesian classifier is a popular type of probabilistic classification algorithm, that estimates the likelihood of a certain data point belonging to a particular class, based on its features. These types of algorithms are often used for sentiment analysis, spam filtering, and document analysis tasks.
- K-nearest neighbors (KNN) algorithm estimates the proximity between different data points to make classifications or predictions about its belonging to a certain class. Its main premise: Similar data points tend to have similar outcomes and thus can be grouped together. KNN can be used both for regression or classification tasks, though it’s more commonly employed as a classification algorithm for use cases such as recommendation systems or image pattern recognition.
- Random forest model uses multiple decision trees (simple yes/no questions) to split the data into different subsets. Each decision tree is trained on a subset of the data and a random subset of features, and then the algorithm aggregates individual predictions into the final decision. This ensemble approach combines classification and regression to produce more accurate and comprehensive prediction results and is often used in various predictive analytics systems.
Supervised Machine Learning Use Cases in Business
Overall, supervised machine learning is the most prevalent type of machine learning deployed due to its high accuracy and relatively straightforward development.
Supervised machine learning also provides a better approach to solving a lot of classical programming tasks such as email spam filtering, financial fraud detection, etc. Instead of programming custom rules (which may not fully cover all the use cases), AI software development teams train the systems to progressively learn over time and produce better outcomes using the provided data.
For example, PayPal uses a supervised tree-based Gradient Boosting Machine (GBM) model to distinguish between legitimate and fraudulent payment transactions. With this model, the company can maintain high payment authorization rates for its customers without increasing risk exposure.
Mailchimp, in turn, relies on machine learning to offer advanced customer segmentation to its users, as well as content recommendations for personalization. As John Humphrey, Head of data platform product at Mailchimp, further explains: “Machine learning is about making sure that you have good data about your customer that you’re feeding the ML algorithms, with a clear objective as to what the customer outcome I wish to predict. That becomes the bedrock of how you get to a place where you can actually start to segment your customers at scale, and have these audiences being generated in lockstep with the content that we can now generate pretty readily as well.”
Other common use cases of supervised machine learning include:
- Customer sentiment analysis
- People analytics
- Spam filtering
- Predictive analytics
- Image recognition
- Fraud detection
Read more about real-world use cases of ML and AI in the FinTech industry.
Unsupervised Machine Learning
Unsupervised machine learning is a method for analyzing unlabeled data to draw conclusions about it. The term “unsupervised” implies that the algorithm independently figures out the target outcome variable (aka some form of insight).
For example, you have a dataset describing different characteristics of your customers (e.g., age, order frequency, average basket size, etc), but you don’t know which customer segments are the most likely to buy from you in the future. Using the available data set, which contains relevant ancillary information (features), an unsupervised learning algorithm draws conclusions about the unknown parameter (likelihood of purchase).
Source: Science Direct
Unsupervised machine learning algorithms are well suited for exploratory data analytics where you want to identify unknown correlations in data. Other common use cases include medical image processing, anomaly detection in equipment performance, marketing audience building, and so on.
The advantages of unsupervised machine learning are:
- High scalability potential — analyze the high volume, velocity, and variety of data without spending as much effort on data labeling.
- Flexible use cases, thanks to the ability to work with unlabelled data and pursue a wide range of exploratory analytical scenarios.
- Lower impact of data quality. Unsupervised algorithms can be effectively trained on datasets with missing values, outliers, and other types of “noise”.
- Unique insights — discover new trends and correlations in large data sets to drive innovation and growth.
The tradeoffs of using unsupervised machine learning techniques, however, are greater algorithm complexity and lower interpretability.
With logic regression, for example, it’s easy to understand how a change in the variable affects the model results. With many unsupervised machine learning techniques, it’s harder to evaluate the accuracy and quality of the obtained clusters or dimensions as the model uses more complex analytical approaches for pattern recognition. In industries like healthcare and finance, where explainability is crucial, this lack of interpretability can be a limitation.
Although unsupervised learning systems can work with extra-large datasets, at some point data scientists get hit by the curse of dimensionality. As the number of data features increases, the amount of data required to effectively learn the underlying structure increases exponentially. Consequently, unsupervised learning algorithms may struggle to analyze high-dimensional data due to data sparsity and increased computational complexity.
Types of Unsupervised Learning Algorithms:
- Associative algorithms identify similarities or associations between different data points, often in the form of itemsets or frequent patterns. The common use cases of associative algorithms such as the Apriori algorithm, Equivalence Class Transformation Algorithm, and Frequent Pattern growth algorithm are customer segmentation, lookalike audience identification, and anomaly detection.
- Clustering algorithms such as K-Means, DBSCAN, and self-organizing maps group similar data points together based on their intrinsic characteristics or proximity in the feature space. Unlike unsupervised classification algorithms, clustering methods don’t need labeled data to classify the inputs. Clustering techniques are often used for image recognition and computer vision tasks, as well as customer analytics.
- Dimensionality reduction is a technique for reducing the number of features in a dataset while preserving important information for analysis. Processing high-dimensional big data requires major computing capacities. Dimensionality reduction methods like Principal Component Analysis (PCA) and Latent Dirichlet analysis (LDA) can reduce the number of dimensions, making it easier to work with large datasets and often revealing new underlying relationships.
Unsupervised Machine Learning Use Cases in Business
Unsupervised machine learning algorithms help find trends that people aren’t explicitly looking for. For example, you can find new profitable customer segments in your dataset or a recurring slump in equipment performance under a certain condition.
In other words: Unsupervised learning helps mine new intel, which often leads to new business opportunities, be it cost reduction or improved asset maintainability. Wayfair, an online home retailer, uses unsupervised algorithms to optimize its supply chain. Based on inventory data, an on-site search algorithm identifies and boosts products that are within a 250-mile radius of the customer.
Beyond that, unsupervised machine learning systems also come in handy for parsing large data sets to get meaning from them faster than with any alternative methods. For example,
Amazon uses an unsupervised sentence-pair comparison model to perform advanced natural language processing (NLP) tasks. JP Morgan, in turn, relies on unsupervised learning to analyze legal documents. The bank’s contract intelligence platform can extract over 150 relevant attributes from 12,000 annual commercial credit agreements in seconds, saving the bank over 360,000 hours per year in manual work.
Other common use cases of unsupervised machine learning include:
- Anomaly detection
- Computer vision tasks
- Speech recognition and synthesis
- Inventory management
- Medical image processing
- Market segmentation
- Personalized recommendations
Discover even more viable applications of ML and AI in EdTech from our previous post.
Reinforcement Learning (RL)
Reinforcement learning (RL) is a subclass of machine learning, focused on optimizing sequential decisions through a rewards-based system.
At a high level, an RL algorithm mimics how humans learn — through trial and error. Unlike the previous machine learning methods, RL algorithms aren’t trained with sample data. Instead, they’re coached to make repeated sequential decisions in a dynamic system under uncertainty. For example, play chess with an opponent.
An RL algorithm learns by interacting with a simulator of the stochastic dynamic system of interest, called a training environment. Every time it makes a decision, it’s either rewarded or punished. The goal of such training is to help the algorithm determine the winning policy for making good decisions over and over again.
Source: Researchgate
The most famous example of reinforcement learning is the AlphaGo algorithm by Google DeepMind, trained to beat the world’s Go champion, Lee Sedol.
AlphaGo Zero, a model trained to play chess, shogi, and Go, learned to play each game by itself, without using human parties for training. Other state-of-the-art reinforcement learning algorithms include:
- Twin Delayed DDPG by Open AI
- Soft Actor-Critic(SAC) by Google and the University of Berkley
- Rainbow Double Q-Learning by the DeepMind team
- Proximal Policy Optimization (PPO) algorithm by OpenAI
Overall, reinforcement learning is seen as one of the most robust, yet challenging fields of machine learning. Such algorithms can power truly novel applications like advanced industrial robots or (semi-)autonomous vehicles.
However, RL systems are also increasingly complex to engineer, train, and validate. Hence, most of the successful commercial applications (so far) have come from Deep Tech companies.
Types of Reinforcement Learning Algorithms
- Markov Decision Process (MDP) is a mathematical framework, which includes a set of states, actions, transition probabilities, rewards, and a policy for the system to make better decisions. The MDP framework is currently used in many applications in robotics and autonomous systems management.
- Bellman Equations express the relationship between the value of a state or state-action pair and the expected cumulative rewards. One equation defines the immediate reward plus the expected value of the next state, while the second one is used to find the optimal policy for maximizing reward over time.
- Q learning method relies on a Q-table, where the agent records the quality of different actions in different states. Through exploration and exploitation, the agent iteratively updates the Q-values, ultimately learning the optimal decision-making policy.
Reinforcement Learning Use Cases in Business
Although complex to implement, reinforcement learning algorithms are increasingly being used by a broad range of applications: retail recommendation systems, time-series forecasting in finance, multi-step logistics optimization, and even clinical trials in healthcare.
Waymo, Google’s sub-division for self-driving cars, uses reinforcement learning to train vehicle agents to navigate challenging driving scenarios such as the high likelihood of collision. DeepMind, in turn, created a self-learning agent for automatically cooling off Google Data Centers. Under AI control, the energy spending in the data center was reduced by 40% without any tradeoffs in safety or performance.
Innovative companies are also replacing an “older” generation of machine learning algorithms with RL systems. Netflix, for example, deployed a new content recommendation algorithm, based on RL, to maximize users’ engagement rates.
Other use cases of reinforcement learning include:
- Industrial automation projects
- Automated trading algorithms
- Dynamic treatment regimes(DTRs) in healthcare
- Natural language processing (NLP) in large language models
- Real-time bidding optimization in ad auctions
Conclusion
Supervised, unsupervised, and reinforcement learning are the trifecta of methods for ML solutions development. Individually and combined, these power most of the current ML use cases across business functions — customer analytics, risk modeling, contact center automation, predictive maintenance, and financial forecasting among others.
That said, each approach has some inherent advantages and shortcomings. When selecting a specific model for your business case, you should factor in its complexity, accuracy, interpretability, training time, and scalability potential among other factors. Deep neural networks, for example, may support more advanced classification, but lack the interpretability of a Naive Bayesian classifier. Likewise, model training time and training data volumes will differ drastically for different methods.
At 8allocate, we’d be delighted to help your company select the optimal machine learning models for the selected use case and facilitate implementation. Contact us for a personalized consultation!
Frequently Asked Questions
Quick Guide to Common Questions
What is machine learning, and why is it important for businesses?
Machine learning (ML) is a method for analyzing data and making predictions using statistical models. It helps businesses optimize processes, automate decision-making, and improve operational efficiency through AI-driven insights.
How does machine learning differ from deep learning and AI?
AI is a broad field focused on creating intelligent systems.
- Machine learning is a subset of AI that enables systems to learn from data.
- Deep learning is a more advanced form of ML that uses neural networks for complex tasks like image recognition and natural language processing.
What are the main types of machine learning?
- Supervised learning: Trained with labeled data for tasks like fraud detection and predictive analytics.
- Unsupervised learning: Identifies hidden patterns in data, useful for customer segmentation and anomaly detection.
- Reinforcement learning: Optimizes decision-making through trial and error, used in robotics and automated trading.
How can businesses apply machine learning for competitive advantage?
Companies can leverage ML for:
- Predictive analytics: Forecast market trends and demand fluctuations.
- Customer intelligence: Improve personalization and engagement.
- Process automation: Reduce manual workload and optimize workflows.
- Risk management: Detect fraud, mitigate cybersecurity threats, and improve compliance.
What are the common challenges in adopting machine learning?
Businesses often face issues like:
- Data quality and availability for accurate model training.
- Integration with legacy systems that lack AI capabilities.
- Lack of skilled ML talent to develop and maintain AI models.
- High computational costs for processing large datasets.
How can companies ensure a successful machine learning implementation?
- Define clear business objectives before selecting ML models.
- Ensure high-quality data for accurate training.
- Start with a pilot project before scaling AI initiatives.
- Continuously monitor and optimize models to improve accuracy.
How does 8allocate help businesses implement machine learning?
8allocate provides end-to-end ML development, ensuring businesses maximize AI-driven automation, data intelligence, and digital transformation. Our expertise includes:
- AI & ML strategy and consulting
- Custom AI model development and integration
- Big Data and predictive analytics solutions
- Cloud-based AI infrastructure for scalability
