Multilayer Perceptron in Machine Learning: The Foundation Behind Every AI Success Story

A Multilayer Perceptron (MLP) is a widely used type of neural network that consists of an input layer, one or more hidden layers, and an output layer. Each is made up of interconnected processing units called neurons. 

They are commonly applied in tasks like pattern recognition and prediction. For instance, an MLP can be trained to detect handwritten digits in banking applications for automating cheque processing with high accuracy.

In this blog, you'll learn how Multilayer Perceptron in machine learning works, its structure, and how it's applied to solve real-world machine learning problems.

Multilayer Perceptron in Machine Learning: How It Works?

What’s unique about MLPs is that they learn complex relationships in your data without being explicitly programmed. You don’t need to define how credit score and income interact. They figure it out through training. Whether you're approving loans, classifying emails, or forecasting sales, MLPs offer a flexible and powerful solution.

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• Masters in Artificial Intelligence and Machine Learning
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Let’s say you work at a bank, and your job is to decide whether a customer’s loan application should be approved or rejected. Sounds like a lot, right? Now imagine if you had a smart assistant, a Multilayer Perceptron (MLP). It could learn from past loan decisions and help you make these calls instantly and accurately.

You want to build a model that looks at:

• Age
• Monthly Income
• Credit Score
• Existing Debt

Overall, it should tell you whether the loan should be approved (1) or rejected (0).

Let’s use this sample applicant’s data:

Step 1: Input Layer – Feeding the Data In

Each feature, age, income, credit score, and debt, acts like a signal going into the MLP. The input layer has one neuron per feature, so you’ve got 4 neurons in this layer.

These values are usually normalized (i.e., scaled between 0 and 1), but we’ll keep the raw numbers for simplicity.

Also Read: ML Types: A Comprehensive Guide to Data in Machine Learning

Step 2: Hidden Layers – The Brain at Work

Your MLP processes this data through hidden layers, which are like little decision-makers inside the network.

Let’s say your MLP has:

• Hidden Layer 1: 5 neurons
• Hidden Layer 2: 3 neurons

Each neuron in these layers takes the input, multiplies it by a weight, adds a bias (kind of like a base value), and then applies a function (usually ReLU or sigmoid) to decide how much of that information should pass through.

For example, imagine one neuron in Hidden Layer 1 does this:

Z = (Age × w1) + (Income × w2) + (Credit Score × w3) + (Debt × w4) + bias  
Z = (35×0.1) + (70000×0.002) + (720×0.05) + (150000×-0.001) + 0.5  
Z = 3.5 + 140 + 36 - 150 + 0.5 = 30  
Output = ReLU(30) = 30

Each neuron does this behind the scenes—and each weight (w1, w2, etc.) gets learned over time.

Step 3: Output Layer – Making a Prediction

Once your data travels through the hidden layers, it lands at the output layer. Here, the network condenses everything it learned into a single value between 0 and 1 using a sigmoid function.

Let’s say it outputs 0.74. That’s like your model saying, “Hey, I’m 74% sure this person should be approved.”

So, if you set your threshold at 0.5, you’d approve this loan.

Also Read: Exploring the Scope of Machine Learning: Trends, Applications, and Future Opportunities

Step 4: Backpropagation – Learning from Mistakes

But what if the true answer was rejection (0), and your model said approval (1)? That’s an error. Your MLP doesn’t like being wrong.

Here’s where backpropagation comes in.

The MLP calculates how far off its prediction was, then adjusts the weights in every neuron backward, from output to input. It uses a method called gradient descent. The goal? Minimize the error next time.

This training continues across thousands of examples until your MLP becomes really good at predicting outcomes.

Step 5: Making Future Predictions

Now that your MLP is trained, you can feed it new loan applications, and it’ll process them just like before through all the layers and output a decision.

All you have to do is supply the numbers. It does the heavy lifting.

Also Read: Top 5 Machine Learning Models Explained For Beginners

Hypothetical Summary with Another Example

Let’s try another applicant:

The MLP processes these inputs, computes the weighted sums, passes them through activations, and finally outputs 0.21. That means there's only a 21% chance this loan should be approved, so the MLP recommends a rejection.

You can get a better understanding of MLP with upGrad’s free Fundamentals of Deep Learning and Neural Networks course. Get expert-led deep learning training, and hands-on insights, and earn a free certification.

Also Read: Top 9 Machine Learning benefits in 2025

Next, let’s look at how a multilayer perceptron in machine learning compares to other models.

MLP vs Other Neural Nets: When to Use What?

Not all neural networks are built the same. Different problems demand different architectures. While Multilayer Perceptrons (MLPs) are a foundational choice in machine learning, they’re best suited for structured data and simpler tasks. But when your data involves images, sequences, or time-dependent context, other networks like CNNs and RNNs might be a better fit.

Knowing when to use what can save you time, compute resources, and frustration. Here's a quick side-by-side comparison to guide your decision-making:

Quick Tip: Use MLPs when your features are independent and your dataset is structured (like spreadsheets or databases). But if the arrangement or order of data matters, it’s time to look at CNNs or RNNs.

Also Read: Neural Network Architecture: Types, Components & Key Algorithms

Next, let’s look at some of the advantages and disadvantages of MLP in machine learning.

Advantages and Disadvantages of Multilayer Perceptron in Machine Learning

Multilayer Perceptron in machine learning is flexible, powerful, and surprisingly effective across a wide range of tasks. Whether you're dealing with regression problems or complex classification challenges, MLPs are often a solid first choice. They excel at modeling non-linear relationships and can approximate almost any function, given enough layers and data.

But as with any tool, they're not without flaws. MLPs can be computationally expensive, sensitive to input scaling, and often lack interpretability. This is true especially when compared to simpler models like decision trees. So while they’re versatile, they’re not always the best fit for every problem.

Here's a quick comparison of their key strengths and weaknesses:

Also Read: 5 Breakthrough Applications of Machine Learning

Next, let’s look at how upGrad can help you understand multilayer perceptron in machine learning.

How Can upGrad Help You Learn Machine Learning Concepts?

Multilayer Perceptron in machine learning continues to play a vital role across different business sectors. It powers everything from fraud detection to recommendation engines. In today’s job market, employers value professionals who understand foundational neural networks like MLPs. They’re often the stepping stones to advanced AI systems.

With upGrad, you can build a strong grasp of MLPs through hands-on projects, industry-aligned courses, and mentorship. They will equip you with job-ready machine learning skills.

In addition to the programs covered above, here are some courses that can complement your learning journey:

• Hypothesis Testing
• Unsupervised Learning: Clustering
• Logistic Regression for Beginners
• Linear Regression - Step by Step Guide
• Introduction to Natural Language Processing

If you're unsure where to begin or which area to focus on, upGrad’s expert career counselors can guide you based on your goals. You can also visit a nearby upGrad offline center to explore course options, get hands-on experience, and speak directly with mentors! 

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Reference:
https://www.sciencedirect.com/topics/computer-science/multilayer-perceptron