Machine Learning Foundations Part 3: Understanding Algorithms

Algorithms: The Pattern Explorers

Think about the great explorers of history, those who ventured into unknown territories, mapping new lands and discovering hidden patterns in nature. These explorers followed systematic methods: studying the stars, tracking seasonal changes, and documenting what they found. Just as explorers once mapped unknown territories, today’s algorithms navigate vast data landscapes, revealing hidden insights that help us understand our complex, interconnected world.

What You’ll Learn in This Series

This is Part 3 of our 4-part journey into machine learning fundamentals. In Part 1, we explored data, the raw observations of our world. In Part 2, we discovered models, the patterns and relationships we seek to understand. Today, we’ll uncover how algorithms, our digital explorers, find meaningful patterns in data and create useful models.

What is an Algorithm?

Think about early astronomers studying the night sky. Like all great explorers, they didn’t just look up and immediately understand the cosmos. They developed careful methods: tracking star positions over seasons, measuring planetary movements, and recording celestial events. These systematic approaches helped them transform countless observations into useful knowledge, much like how modern algorithms explore data to discover hidden insights.

Modern algorithms follow this same spirit of exploration, but they can process millions of observations simultaneously. When navigation apps analyze traffic patterns to find the fastest route, or when weather systems predict storm paths by analyzing atmospheric data, they’re using algorithms that have explored vast territories of data, more than any human explorer could cover in a lifetime.

The Dance of Data, Models, and Algorithms

Remember our constellation analogy from Part 2? Let’s extend it. If data points are the stars scattered across the night sky and models are the constellations we recognize, then algorithms are the methods astronomers developed to decode these celestial patterns. Just as ancient astronomers connected dots between stars to form pictures in the sky, algorithms connect data points to discover patterns that tell us something meaningful about the world.

The Babylonians, for example, kept detailed astronomical diaries on clay tablets dating back to around 750 BCE. They meticulously recorded the positions of planets, the timing of eclipses, and the appearance of comets. Over generations, they used these systematic observations to create mathematical models that could predict celestial events. When their tablets predicted an eclipse would occur at a certain time and place, reality would test their understanding. Similarly, modern algorithms test their patterns by making predictions about new data and adjusting when they get things wrong.

Let me share a real example that shows this in practice. I once worked on a project to predict equipment failures in a factory. While not as poetic as mapping stars, it followed the same fundamental process of pattern discovery:

Data: Thousands of sensor readings, maintenance records, and performance logs
Algorithm: A systematic process that looked for subtle signs of future failures
Model: The resulting framework that could predict when machines needed maintenance

At first, our algorithm found random-seeming correlations in the factory data, such as linking machine failures to the day of the week rather than actual equipment stress patterns. We nearly scrapped the whole project when we saw it predicting higher failure rates on Wednesdays, which made no logical sense. But as we refined our approach and tested our assumptions, genuine patterns emerged. We discovered that subtle vibration changes, combined with temperature fluctuations, consistently preceded equipment failures by several days. Specifically, when bearing vibrations increased by 20%, and the ambient temperature rose more than 5 degrees, equipment failure followed within 72-96 hours. This allowed us to prevent costly breakdowns through early intervention by scheduling maintenance exactly when needed. Even better, once we understood these true patterns, we could track similar metrics across different types of equipment. This journey from misleading correlations to meaningful patterns showed us that exploring data, like any exploration, requires patience, skepticism, and careful observation.

The Critical Connections: How It All Works Together

Just as astronomers need clear skies, precise instruments, and proven methods to map the stars, machine learning needs quality data, appropriate algorithms, and well-structured models to succeed. Let’s see how these pieces fit together:

Data Shapes Algorithms

Think about trying to map stars through cloudy skies, it simply won’t work, no matter how good your methods are. Similarly, algorithms need well-structured data to find meaningful patterns:

Data must be consistent (like measuring star positions using the same reference points)
Values should be comparable (imagine trying to combine star measurements where some used degrees and others used radians)
Missing or incorrect information needs to be handled (like accounting for nights when certain stars weren’t visible)

I learned this the hard way in my early career while working on a customer project. Our algorithm kept making bizarre predictions until we realized our data mixed European dates (day/month) with American ones (month/day). The algorithm couldn’t find real patterns because the data wasn’t consistently structured.

Algorithms Shape Models

Different algorithms will find different types of patterns in the same data, just as different astronomical methods might emphasize different aspects of the night sky:

Some algorithms look for simple, clear patterns (like finding the Big Dipper)
Others search for complex, subtle relationships (like mapping the slight wobble of stars that reveals hidden planets)
The algorithm you choose determines what kind of model you’ll get

For example, when analyzing customer behavior, a simple algorithm might create a model that groups customers by how much they spend. A more complex algorithm could create a model that captures subtle patterns in how spending changes over time, what products are bought together, and how customers respond to different marketing approaches.

Models Shape Data Needs

As your models evolve, they often reveal the need for different or additional data:

Initial models might show that certain data points are crucial while others are irrelevant
Models might reveal gaps in your data collection
The precision of your model often depends on the quality of your data

In my factory prediction project, our initial model revealed that we needed more detailed vibration data to accurately predict equipment failures. The model showed us exactly what kind of data would make our predictions better.

The Continuous Cycle

These elements work in a continuous cycle of improvement:

Better data leads to more effective algorithms
Better algorithms create more accurate models
Better models help us understand what data we need
And the cycle continues…

The cycle essentially describes a virtuous feedback loop in data science and machine learning, where each element enhances the others. It’s a concise representation of how modern machine learning and data analysis evolve.

How Algorithms Work: A Deeper Look

Let’s return to our astronomical theme. Early astronomers followed a methodical process:

Made careful observations
Proposed explanations for what they saw
Tested their theories against new observations
Refined their understanding
Shared their improved methods with others

Modern algorithms follow remarkably similar steps:

Examine the available data
Form initial patterns or hypotheses
Test these patterns against new data
Adjust based on successes and failures
Continuously improve their accuracy

The key difference? Speed and scale. While ancient Babylonian astronomers would record individual planetary positions night after night, creating their predictions from roughly 50 observations per year, modern algorithms can process millions of astronomical measurements per second. For perspective, when today’s algorithms analyze telescope data, they can track thousands of celestial bodies simultaneously across multiple wavelengths of light, comparing current positions with billions of historical data points. What took ancient astronomers generations to discover about a single planet’s motion, a modern algorithm can learn about thousands of celestial objects in minutes.

Let’s consider a specific example. In the 1980s, astronomers would spend weeks analyzing images from space telescopes to identify a few dozen new celestial objects. Today, algorithms process data from sky surveys in real-time, discovering hundreds of new asteroids, variable stars, and potential exoplanets every night. They can simultaneously track position, brightness, temperature, chemical composition, and dozens of other variables for each object, something no ancient astronomer could have imagined possible.

Types of Algorithms

Just as we have different ways of studying the cosmos, we have different types of algorithms for finding patterns in data:

Supervised Learning Algorithms

These are like having a master astronomer point out constellations. “See those stars? That’s the Big Dipper.” The algorithm learns from labeled examples, just like a student learning from a teacher’s guide. Imagine teaching someone to identify dogs by showing them thousands of dog photos labeled “dog” and non-dog photos labeled “not dog.” After seeing enough examples, they learn the features that make a dog a dog. A modern example is teaching a medical system to identify diseases by showing it thousands of diagnosed X-rays. The system learns which visual patterns indicate pneumonia versus a healthy lung, much like a radiologist learned during their training. The “supervised” part means we’re providing the correct labels during training—we tell it “this X-ray shows pneumonia” or “this X-ray is healthy.”

Unsupervised Learning Algorithms

Imagine being the first human to look at the night sky with no preconceptions. You’d naturally start to see patterns: clusters of stars, bright regions, and dark spaces, all without anyone telling you what to look for. These algorithms are like that first stargazer, discovering patterns in data on their own. For example, if you gave an unsupervised algorithm data about customer purchases, it might discover natural shopping patterns without being told what to find. It could reveal that some customers buy mostly late at night, others are weekend shoppers, and others shop in small amounts but frequently. The algorithm discovers these groups by itself, similar to how you might naturally sort your music into workout songs, relaxing music, and party tunes without anyone teaching you these categories.

Deep Learning Algorithms

These are like having a thousand astronomers working together, each spotting different patterns and combining their insights. But rather than working as one big group, imagine they’re organized in teams, each team building layers on what the previous team discovered. The first team looks for the simplest patterns, like bright spots and lines. They pass what they find to the next team, who combine these simple patterns into more complex shapes. After that, each team works with increasingly sophisticated patterns until the final team can understand entire celestial events.

This is exactly how deep learning algorithms work. When looking at a photo of a dog, for instance, they start simple and build up understanding step by step:

First layer: Finds basic patterns like edges and colors
Next layer: Combines these into simple shapes like circles and curves
Another layer: Puts shapes together into features like ears, eyes, and tails
Final layer: Understanding “this is a dog playing in a park”

This step-by-step building of understanding from simple to complex is why they’re called “deep” learning algorithms. It’s also why they’re so powerful—they can tackle complex tasks like recognizing speech in noisy rooms, translating languages in real-time, or turning your spoken words into accurate text messages.

While we’ve covered the three most common categories of learning algorithms, there are other specialized types that build upon these foundations. Just as astronomers have developed various methods to study different celestial phenomena, data scientists have created other types of algorithms for specific challenges. But understanding these three fundamental categories gives us a strong foundation for exploring how algorithms find patterns in data to create useful models.

From Learning to Predicting: Putting Models to Work

Think about how ancient sailors used their star maps. After astronomers spent generations learning patterns in the night sky, sailors could use these celestial models to predict their location and navigate vast oceans. They didn’t need to rediscover the patterns – they simply applied what was already learned.

This is exactly how machine learning models work in practice. Once an algorithm has discovered patterns in the data, the resulting model can make predictions about new situations. We call this inference. It means using learned patterns to understand new data.

How Prediction Works

Let’s see this in action with our factory example:

First, our algorithm analyzed historical data to learn patterns: which sensor readings and maintenance records preceded equipment failures.
Now, when we get new sensor readings from a machine, our model can predict if it’s likely to fail soon.
We don’t need to run the full learning process again – the model applies its learned patterns to new data.

It’s like a sailor using star charts rather than having to map the stars themselves. The hard work of pattern-finding has already been done.

Real-World Examples

The prediction phase is where machine learning brings tangible value, allowing us to apply learned patterns to new situations and make informed decisions. For example:

A trained medical diagnosis model can spot potential issues in new X-rays in seconds, helping doctors make faster, more accurate diagnoses
A customer behavior model can predict which products a new customer might like based on their first few purchases
A trained language model can understand and respond to questions it hasn’t seen before by applying patterns it learned from millions of conversations

I once worked with a retail model that predicted store inventory needs by analyzing weather forecasts. The algorithm learned historical patterns linking weather conditions to shopping behavior and applied those insights to new situations. For example, when an unexpected heat wave was forecasted, the model recommended stocking up on specific items—and it was spot on.

When Predictions Go Wrong

Just as clouds can obscure the stars and lead to navigation errors, predictions can fail when:

New data is too different from what the model was trained on—like using star charts for the Northern Hemisphere to navigate in the Southern Hemisphere
The patterns have changed—imagine if the stars suddenly shifted positions
The input data is incomplete or incorrect, leading to faulty conclusions, like navigating with a broken compass

The Feedback Loop

The best systems include a way to track prediction accuracy and use this information to improve:

Monitor how often predictions are correct
Collect new data about missed predictions
Periodically retrain models with updated data

Just as astronomers refined their star charts based on sailors’ experiences, we continuously improve our models based on their real-world performance.

The Process of Discovery

The beauty of algorithms lies in their ability to uncover patterns hidden from human perception. During a recent data analysis project, I witnessed how algorithms can reveal subtle connections, much like a detective piecing together seemingly unrelated clues.

In one memorable instance, our team discovered a fascinating pattern that initially seemed misleading. An early machine learning model identified a strong statistical correlation between two variables that appeared completely unrelated. At first glance, the connection seemed absurd: the model suggested a link between the number of fire stations in a city and the city’s crime rate.

A less careful analysis might have jumped to bizarre conclusions. But our team dug deeper. We realized the correlation wasn’t about causation, but about underlying urban infrastructure. Cities with more complex urban environments tend to have both more extensive emergency services and more comprehensive crime reporting systems. The relationship wasn’t causal. It was a symptom of urban complexity.

This experience reinforced a critical lesson: algorithms are powerful pattern-recognition tools, but they require human insight to translate raw data into meaningful understanding. Numbers alone don’t tell a story – context, critical thinking, and domain expertise transform statistical patterns into genuine insights.

The key isn’t just finding patterns, but understanding the complex systems that generate those patterns. Algorithms can illuminate hidden connections, but human wisdom interprets their true significance. This brings us to some common questions people ask about algorithms’ capabilities and limitations.

Common Questions About Algorithms

People often ask: “Do algorithms think like humans?” The short answer is no. Algorithms are sophisticated computational tools that process information through a series of carefully defined steps. Think of them as incredibly fast, precise recipe followers that can work with massive amounts of data.

What is Algorithm Training?

Imagine teaching a child to recognize different animals. At first, you’d show them pictures and say, “This is a dog” or “This is a cat.” Over time, the child learns to identify key features like floppy ears, tail shape, and size that distinguish one animal from another. Algorithm training works similarly.

Training is the process of teaching an algorithm to recognize patterns by exposing it to large amounts of example data. Just like a child learns from repeated examples, algorithms learn by analyzing thousands or even millions of data points. During training, the algorithm receives input data, makes initial predictions, compares those predictions to correct answers, and adjusts its internal mathematical models to become more accurate.

For instance, a photo recognition algorithm might be trained on millions of labeled dog photos. Each time it incorrectly identifies an image, it slightly modifies its understanding of what makes a “dog” a dog. Over time, it becomes incredibly precise at distinguishing dogs from other animals.

Unlike human thinking, which is fluid and intuitive, algorithms operate through systematic processes. They don’t understand patterns the way we do; instead, they identify patterns by methodically comparing data points, testing hypotheses, and refining their approach.

Can algorithms be wrong? Absolutely. Just as early astronomers sometimes saw patterns that weren’t really there (like thinking the sun orbited Earth), algorithms can find misleading patterns too. This happens because algorithms rely on the data they’re given, follow strict mathematical rules, lack the broader contextual understanding humans possess, and can be influenced by biases in their training data.

For example, an algorithm trained on historical hiring data might inadvertently perpetuate past discriminatory practices if the original data reflected biased hiring decisions. This is why human oversight and careful data preparation are crucial in developing reliable algorithms.

The key to good algorithms is not just their computational power, but the human wisdom that guides their creation, trains their learning, and interprets their results.

Why This Matters: Learning at Scale

We’ve come a long way from mapping stars to mapping data. Today, algorithms find patterns in everything—from the way galaxies form to the way cities grow. They help farmers predict optimal planting times, assist architects in designing energy-efficient buildings, and enable manufacturers to reduce waste in their production lines. These advancements have transformed how we work, live, and understand our world.

But just like any tool, their power depends on how we use them. Understanding how algorithms learn at scale helps us know when to trust their insights and when to question their conclusions. Algorithms can be incredibly accurate, but they also have limitations. They might miss the broader context, fail to recognize underlying assumptions, or not account for changes over time, leading to incorrect predictions or outcomes. By being mindful of these nuances and how algorithms learn from vast amounts of data, we can avoid potential pitfalls and ensure that these powerful tools are used effectively and responsibly.

Quick Exercise: Algorithms in Action

Next time you’re outside at night, try to find the Big Dipper. Notice how your brain follows steps: you scan the sky, look for bright stars, and connect them in the familiar pattern. What you’re doing is running a pattern recognition algorithm! You’re recognizing a pattern among the stars, just like how machine learning algorithms work with data.

Now, imagine doing this with millions of stars all at once, processing vast amounts of information, finding patterns, and connecting data points. This is similar to how supervised learning models work – they take labeled data, recognize patterns from these examples, and learn to predict new situations. Unsupervised learning models, on the other hand, find hidden patterns without labeled data, grouping similar data points together to uncover relationships. Deep learning models go even further by mimicking the human brain’s ability to recognize complex patterns across multiple layers of data.

This ability to analyze large sets of data, find patterns, and make predictions is what allows machine learning to have a significant impact in fields like medicine, climate science, and finance.

While we’ve explored how algorithms help us find patterns and build models from data, it’s important to remember that these algorithms run as software programs on computers. Just like we use specific tools for different exploration tasks, data scientists use different programming languages and software tools to implement these algorithms. And just as telescopes have evolved from simple spyglasses to powerful modern observatories, the computers that run these algorithms have grown increasingly sophisticated.

Coming Up Next

This brings us to our final installment, Part 4, where we’ll explore the hardware that powers these algorithms. From early computers that took hours to process simple patterns, to today’s massive data centers finding complex patterns in real-time, we’ll see how advances in hardware have enabled new types of algorithms and models.

See you in Part 4, where we’ll discover how modern computers turn these algorithmic steps into reality. Remember, data provides the observations, algorithms discover the methods, and models capture the resulting patterns. Together, they allow us to uncover new insights and drive innovation across every field of human endeavor—from exploring distant planets to preserving endangered species, from optimizing renewable energy to creating more sustainable cities—reshaping our understanding of the world.