1. Introduction to Machine Learning – A Simple, Layman-Friendly Explanation

Let’s start with a simple question: How do we, as humans, make decisions?

Think about it. Whether you’re deciding what to eat, which route to take to work, or when to water your plants—you’re using past experiences to make informed choices. Some of those experiences are your own, some come from observing others, and some you may have read or heard about. Your brain processes these data points, weighs the pros and cons, and then makes a decision.

Your brain, in essence, is a very powerful decision-making machine. It collects data, stores it, and processes it using logic and reasoning.

Now imagine this same process happening inside a computer system. Imagine feeding it past experiences—like data from transactions, temperatures, customer feedback, or even sensor readings—and training it to recognize patterns and make decisions on its own.

That’s machine learning.

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What is Machine Learning?

Machine Learning (ML) is a type of artificial intelligence (AI) where computers learn from historical data and use that knowledge to make predictions or decisions—just like humans do from experience.

If we give a machine enough relevant data, it can begin to recognize patterns and predict outcomes, often more accurately and at scale than any human could.

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Two Main Types of Problems in ML

Let’s look at two everyday decision types, and how they relate to machine learning.

1. Classification – Making Yes/No or Group Decisions

Imagine you’re picking apples at the market. You look at the color and smell the fruit. Based on that, you decide whether it’s good or bad. You’re classifying the fruit into two categories: “buy” or “don’t buy.”

In machine learning, classification works the same way. We train a model to recognize input patterns and assign them to predefined groups.
Examples: Spam or Not Spam, Fraud or No Fraud, Passed or Failed.

2. Regression – Estimating Continuous Values

Now, suppose you want to price that apple. Is it worth 1 euro? 2 euros? The price can be any number—it’s not a category.
This is where regression comes in. It helps predict continuous outcomes based on the input data.
Examples: Predicting house prices, forecasting stock trends, estimating delivery time.

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Types of Machine Learning

The approach we take in machine learning depends on what kind of data we have. Broadly, ML is divided into three main types:

1. Supervised Learning

This is the most common form of machine learning.
We use labeled data—meaning each example has a clear input and output.
Example: A student dataset with inputs like study hours and attendance, and outputs like Pass/Fail.

The model learns the relationship between the features and the result so that it can predict outcomes on new data.

Some popular supervised learning algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM)

2. Unsupervised Learning

Here, the data doesn’t have labeled outputs. Instead, the goal is to find hidden patterns or groupings in the data.

Example: Imagine you have sales data for thousands of customers but no labels like “loyal” or “occasional.” You can use algorithms like K-Means Clustering to segment customers based on their behavior.

Common applications:

• Customer segmentation
• Market basket analysis
• Anomaly detection

3. Semi-Supervised Learning (less common)

This is a mix of both supervised and unsupervised learning. You have a small amount of labeled data and a large amount of unlabeled data. It’s used when labeling data is expensive or time-consuming.

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Final Thoughts

It’s estimated that over 90% of real-world ML applications today are supervised learning problems. From fraud detection to email filtering, personalized ads to product recommendations—most use labeled data to train predictive models.

At its core, machine learning is about using the past to predict the future. It finds patterns in historical data and applies that knowledge to new, unseen scenarios.

In future blogs, I’ll dive deeper into specific algorithms, when to use them, and how they work behind the scenes.

If you’re just starting out—remember this:
Machine learning isn’t magic. It’s about learning from data, just like we humans do. It will make mistakes, like we will do. So, evaulating the model is the most crucial part. We discuss next.