Skip to main content

Introduction to Supervised Machine Learning: Step-by-Step Guide for Beginner

 

What is Supervised Machine Learning?

Supervised Machine Learning is training a computer in the same way that we train children using examples.

Let’s break it step by step

Example with Fruits

Consider a scenario where you are training a child on how to identify fruits.

  1. You show an apple and say, “This is an apple.”

  2. You show a banana and say, “This is a banana.”

  3. After watching lots of apples and bananas the child begins to notice the patterns:

    • Apples are usually round, red, or green.

    • Bananas are long and yellow.

Next time even when you give the child a new apple (not the one you showed him or her before) the child is able to say: “That looks like an apple!”

How Computers Learn in the Same Way

In supervised ML:

  • Input (Data) = The items that we present to the computer (such as images of fruits, emails or medical records of patients).

  • Output (Label/Correct Answer) = What the data really is (Apple, Banana, Spam, Not Spam, Healthy, Sick).

  • Learning Process = The computer will examine lots of examples and attempts to identify patterns that relate input to output.

Later, we feed the computer with some sort of new input it has never encountered before, it can now make predictions on what it has learned the correct output to provide.



Why is it called "Supervised"?

The word supervised comes from the fact that:

  • The computer is not learning blindly.

  • During training we do provide it with the right answers (labels) just as a teacher would do with student.

    The computer would be unable to know whether it is doing the prediction right or wrong without the answers in the correct form.

Imagine that you have a teacher (labels) who controls the learning process.

A Simple Real Life Example:

  • Student’s Homework: A student solves math problems (inputs).

  • Answer Key: The teacher gives the correct answers (labels).

  • Learning: The student compares his/her work with the answer key and learns mistakes.

  • Exam Day: The student receives new problems (unseen inputs) and applies what he or she has learned to this new problems in order to solve them in a right way.

And that is what supervised machine learning does!

So simply we can say  "Supervised Machine Learning is that we monitor the learning with the right answers (labels)."

Steps in Supervised Learning

Let's talk about all steps:

  1. Collect Data

    • Sample: Thousands of spam emails or spam not emails.

  2. Preprocess Data

    • Cleaning the data like remove errors, fill missing values etc.

    • Text or Images datsets are converted into numbers so that computer can understand.

  3. Split Data

    • Training Set, like 80% of total dataset → This is used to train the model.

    • Testing Set, like 20%  of total dataset → Used to check if the model learned well.

  4. Train the Model 

    • The algorithm (Model) discovers patterns between the inputs and the outputs.

  5. Test/Evaluate the Model

    • Accuracy of checks on new data (testing set).

  6. Improve the Model

    • It is now time to do it with more relevant data, or experiment with better tuning parameters or with more suitable algorithms.

Common Algorithms in Supervised Learning

Let’s quickly explore the most popular ones (no heavy math, just intuition):

Challenges in Supervised Learnin

Real-World Applications

Uploading: 156482 of 156482 bytes uploaded.

Advantages, Limitations & Future Scope 

Conclusion

Supervised Machine Learning is an example-based teaching. . It drives numerous systems in the real world in predicting house prices, and in the detection of diseases. 

Simple models such as Linear/Logistic Regression can also be used by the beginners, and, subsequently, there are more complicated models, which include Random Forests and Neural Networks.

The key is good data + right algorithm + careful evaluation.


Thanks for reading 💗!


If you found this post useful:

⮕  Share it with others who might benefit.
⮕  Leave a comment with your thoughts or questions—I’d love to hear from you.
⮕  Follow/Subscribe to the blog for more helpful guides, tips, and insights.


Comments

Post a Comment

Popular posts from this blog

Artificial Intelligence vs Machine Learning vs Deep Learning: Key Differences Explained

  Everywhere you look today, people are talking about AI, Machine Learning, and Deep Learning . Tech companies use these terms in product launches, news headlines throw them around, and chances are you’ve already heard them in your classroom, workplace, or even casual conversations. But here’s the catch,  most people don’t actually know the difference . Some think AI, ML, and DL are the same thing. Others assume they’re just fancy names for robots or algorithms. So, what really sets them apart? Is AI just about robots? Is Machine Learning smarter than AI? And why does everyone say Deep Learning is the future? In this blog, we’ll break down introductory part of  AI, ML, and DL in simple and easy language with examples, diagrams, and real-life applications — so by the end, you’ll never be confused again. Think of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) like math sets . AI  is the  biggest set (Universal Set) ...
Post a Comment
Read more

All About Inheritance in OOP: Explained with Real-Life Examples

  The first thing that comes to mind when  you hear the word  inheritance,  is passing something down from parents to children . In Object-Oriented Programming, it works in a very similar way—it allows one class (child class) to inherit properties and behaviors from another class (parent class). This concept makes code reusable, organized, and easier to maintain . Let’s explore inheritance step by step with real-time analogies to make it super simple. What is Inheritance in OOP? In OOP, inheritance is a concept in which a class inherits the properties (variables) and behaviors (methods) of other classes. Common features are defined in the parent class . These features are extended by the child class which is capable of adding its own features.. It is best to consider it as a family tree : a child takes after a parent in terms of eye color or height but can also possess an individual characteristic. Why Use Inheritance? Code Reusability – No need t...
Post a Comment
Read more

Why Python Is the Best Programming Language for Machine Learning, AI, and Deep Learning

  Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have become some of the most transformative technologies of our time. From self-driving cars and recommendation systems to chatbots and healthcare diagnostics, these technologies are reshaping industries at an incredible pace. But have you ever wondered: Why do most AI researchers, data scientists, and developers prefer Python over other programming languages? In this blog, we’ll explore in depth why Python has emerged as the most popular and powerful language for AI, ML, and DL development . 1. Simplicity and Readability – Focus on Problem Solving, Not Syntax Complex mathematics and algorithms are some of the greatest challenges that newcomers in the world of AI/ML face. Python eases this load by providing a clean syntax that is easy to read. Let’s look at three concrete examples where Python’s clean syntax helps newcomers in AI/ML handle complex mathematics and algorithms more easily compared to ot...
Post a Comment
Read more