Traditional Machine Learning (ML) is the backbone of many technologies we use every day. The central idea of Machine Learning is teaching a machine to learn from data and make predictions or decisions without being explicitly programmed for every scenario.
Traditional ML models can be broadly categorized into three types of learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Each has its strengths, and companies around the world are using them to tackle real-world problems.
1.1 Supervised Learning: When Labeled Data Is King
Supervised Learning is the most commonly used type of Machine Learning. Here, we have “labeled data,” meaning the data comes with a correct answer (or outcome) that the model is trying to learn to predict. Imagine teaching a child to recognize animals. You show them pictures of cats and dogs, and after enough examples they learn to tell them apart. Supervised Learning works the same way.
Example 1: Predicting Loan Defaults in Banking
In banking, Supervised Learning is used to predict loan defaults. Banks want to minimize the risk of lending money, so they analyze historical data of borrowers—age, income, debt levels, credit score, and whether they defaulted or repaid their loans.
The Machine Learning model learns to predict the probability of a new applicant defaulting by understanding the relationship between the features (income, credit score, etc.) and the outcome (default or no default). A Logistic Regression is a simple algorithm that predicts binary outcomes (like yes/no) by estimating the probability of an event based on input features. A Random Forest Algorithm is a powerful algorithm that combines multiple decision trees to make accurate predictions, which is especially effective with complex or messy data, and can be applied here as both are great at handling structured data and predicting binary outcomes. This helps banks approve loans more wisely, reducing the risk of defaults.
Example 2: Image Classification in Healthcare – Identifying Tumors
One impactful use case of Supervised Learning is in Image Classification for healthcare. Let’s say we have thousands of images of chest X-rays, with each labeled as either showing signs of cancer or not. A Convolutional Neural Network (CNN) can be trained to recognize subtle differences in these X-rays. Over time, the model becomes highly accurate in spotting early signs of cancer.
Google’s DeepMind has pioneered such models in radiology, where they outperform human doctors in certain diagnostic tasks, such as detecting early-stage lung cancer from CT scans. These models can scan thousands of images in a fraction of the time, improving early detection and saving lives.
Example 3: Sentiment Analysis in Social Media Monitoring
Supervised Learning is also widely used in Natural Language Processing (NLP). Imagine a brand monitoring its reputation on social media. With a supervised ML model trained on a labeled dataset of social media posts (labeled as positive, neutral, or negative), the company can classify new posts to understand public sentiment.
For instance, a company like Coca-Cola might use a sentiment analysis tool to monitor how people feel about their latest ad campaign. Tools like these can help brands respond quickly to negative feedback, refine their messaging, and measure the success of their marketing strategies in real time.
1.2 Unsupervised Learning: Unlocking Hidden Patterns
In Unsupervised Learning, the data does not have labeled outcomes, so the machine is left to find hidden structures on its own. This is especially useful when you want to explore data without knowing exactly what you’re looking for. Unsupervised Learning helps businesses segment customers, detect anomalies, and discover relationships between data points.
Example 1: Market Basket Analysis in Retail – Discovering Customer Habits
One of the most famous uses of Unsupervised Learning is in Market Basket Analysis, used by retailers to understand customer buying behavior. Ever wonder how online retailers like Amazon suggest “Frequently Bought Together” items? That’s Unsupervised Learning in action!
A model called Association Rule Learning—specifically the Apriori algorithm—can analyze millions of purchase transactions and find patterns. For example, if customers often buy bread and milk together, the store may place these items close to each other or offer discounts on these pairs.
Walmart famously used this technique to discover that when hurricanes were forecasted, people bought more Pop-Tarts. So they stocked Pop-Tarts near bottled water before hurricanes, increasing sales during such events.
Example 2: Fraud Detection in Finance – Finding Anomalies
Unsupervised Learning is also used for Anomaly Detection, particularly in finance. In credit card transactions, fraud detection models typically don’t have labeled examples of all possible types of fraud. The machine learns from the normal behavior of transactions—things like where and when the card is used, the amount spent, and the frequency of purchases. When a transaction looks unusual (like a sudden large purchase from a foreign country), the model flags it as potentially fraudulent.
Clustering algorithms like K-means or DBSCAN help group similar transactions together, and anything that doesn’t fit into the clusters is flagged as an anomaly. This real-time fraud detection system helps financial institutions quickly detect and prevent fraud without needing explicit examples of every kind of scam.
Example 3: Content Recommendation in Streaming Services
Unsupervised Learning also powers Recommendation Engines used by streaming services like Netflix or Spotify. These services group users into clusters based on viewing or listening habits. For example, if you’ve watched a lot of sci-fi movies, Netflix may cluster you with other sci-fi fans and recommend movies that are popular in that group.
These algorithms often use Collaborative Filtering, which looks for patterns in user behavior without explicit labels. So if 100 people who watched “The Expanse” also enjoyed “Altered Carbon,” the algorithm will recommend it to you as well. This clustering technique enhances user experience by offering personalized suggestions.
1.3 Reinforcement Learning: Learning Through Experience
Reinforcement Learning (RL) is quite different from Supervised and Unsupervised Learning. It’s about learning through interaction with an environment. The machine makes decisions, receives feedback (positive or negative), and learns through trial and error. This approach is particularly useful for decision-making tasks where the environment is dynamic and complex.
Example 1: Gaming AI – Mastering Complex Games
A breakthrough example of reinforcement learning is AlphaGo, developed by DeepMind. Go is an ancient Chinese board game with more possible moves than atoms in the universe. Traditional ML approaches struggled with this, but AlphaGo learned by playing millions of games against itself. Each time it made a successful move, it was rewarded, and when it failed, it was penalized. Over time, it learned optimal strategies and became the first AI to beat a world champion at Go, a feat that many thought would take decades.
Reinforcement Learning is now widely used in gaming AI. In games like chess or StarCraft, the AI doesn’t need to be explicitly programmed with strategies, it learns through playing and improves on its own.
Example 2: Autonomous Robots in Warehouses
In warehouse automation, companies like Ocado and Amazon use robots to pick, pack, and transport items. These robots are powered by Reinforcement Learning algorithms that learn how to navigate complex warehouse environments efficiently. Every time the robot completes a task (like reaching a product shelf), it’s rewarded, and when it fails (like hitting an obstacle), it learns to adjust its behavior.
The goal is for the robots to learn the most efficient path from one point to another in real time, which saves companies millions of dollars in logistics costs.
Example 3: Portfolio Management in Finance
Reinforcement Learning (RL) is also finding its way into Portfolio Management in finance. Hedge funds and financial institutions use RL to make investment decisions in dynamic markets. The algorithm learns how to optimize returns by continuously adjusting the portfolio based on feedback from the market. The rewards come in the form of profits, and losses act as penalties. Over time, the model can develop strategies that outperform traditional investment approaches by learning from market behavior.
Key Algorithms in Traditional Machine Learning
Let’s also touch on the algorithms behind these use cases to understand why they are so powerful:
- Linear Regression: These are used for predicting continuous outcomes, like housing prices or stock returns.
- Decision Trees & Random Forests: These are highly interpretable models that can be used for both classification (e.g. predicting customer churn) and regression (e.g. predicting sales numbers).
- K-means Clustering: This is the go-to algorithm for Unsupervised Learning, often used for customer segmentation.
- Support Vector Machines (SVMs): These are great for tasks like image recognition and text classification when you need a robust model with high accuracy.
- Neural Networks: These are used in everything from facial recognition to predicting consumer behavior; Neural Networks mimic the way the human brain processes information.
Conclusion: The Strength of Traditional ML
Traditional ML’s power comes from its versatility and ability to make sense of vast amounts of data. Whether predicting stock prices, detecting fraud, or even driving autonomous vehicles, traditional ML models are crucial for decision making and optimization across industries. From healthcare and finance to retail and logistics, companies that adopt these technologies are gaining a competitive edge, improving efficiency, and unlocking new capabilities. Additionally, traditional ML models offer a level of determinism and repeatability, meaning they consistently produce the same results given the same data, making them reliable and transparent for business-critical applications.
In the next segment, we’ll move on to Generative AI (GenAI), which takes things a step further by creating entirely new content from scratch—whether it’s writing articles, composing music, or generating images. Stay tuned for a look at how this creative side of AI is transforming industries!
