Machine Learning vs Neural Networks: Understanding the Key Differences

Did you know that AI applications are already impacting your daily life? In fact, according to a report by McKinsey, more than 70% of businesses are already using AI in one form or another, with machine learning and neural networks at the heart of it.

But are they the same? Here's the catch — though these terms are often used interchangeably, machine learning vs neural networks are not the same. Understanding their subtle yet critical differences can empower you to take your skills to the next level.

This article will break down the core differences, why they matter, and how they work together in applications you use every day. Let's dive in!

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What is Machine Learning?

Simply put, machine learning is a branch of AI that allows computers to learn from data and make decisions without being explicitly programmed. Instead of following pre-defined rules, machines use patterns and inferences from historical data to predict outcomes, improve over time, and automate tasks.

Let's dive deeper into its core components.

Types of Machine Learning

Machine learning isn’t just one-size-fits-all; it comes in several types, each suited to specific tasks and data. The three main types are:

• Supervised Learning

This model is trained using labeled data (already categorized data). The goal is for the machine to make predictions based on this data.

Example: Predicting house prices based on features like size, location, and number of bedrooms.

• Unsupervised Learning

Unsupervised works with unlabeled data. The machine tries to find hidden patterns or structures within the data without any explicit guidance. 

Example: Customer segmentation for targeted marketing.

• Reinforcement Learning 

This model learns by interacting with its environment and receiving rewards or penalties based on its actions. It’s like trial and error, where the machine learns to improve its performance. 

Example: A robot learning to navigate a maze.

Let's see how ML transforms industries with its powerful real-world applications.

Explore the ultimate comparison—uncover why Deepseek outperforms ChatGPT and Gemini today!

Key Applications of Machine Learning

Machine learning is everywhere, and here are some of the glimpses of it:

• Recommendation Systems: Netflix or Amazon use ML to analyze your past behavior and recommend movies, products, or shows.
• Fraud Detection: Banks use ML algorithms to detect unusual transactions and prevent fraud.
• Autonomous Vehicles: Self-driving cars rely heavily on ML to interpret sensor data and make real-time decisions.
• Healthcare: ML is used to diagnose diseases, predict patient outcomes, and personalize treatment plans.

With these applications in mind, it’s clear that ML is reshaping industries and has a transformative impact on our world.

Also Read: Top Advantages and Disadvantages of Machine Learning in 2024

Now, let's dive into neural networks and understand how it differs from general machine learning.

What is a Neural Network?

If machine learning is the brain behind AI, neural networks are its neural connections — inspired by how the human brain works. Just as our brain uses neurons to learn from information, a neural network uses layers of interconnected nodes (or "neurons") to recognize patterns and learn from data.

Let’s look at their core components to understand how neural networks work. 

Also Read: Neural Network Model: Brief Introduction, Glossary & Backpropagation

Key Components of Neural Networks

These are the building blocks that allow them to process information and learn from data, much like our brains.

• Nodes (Neurons): These individual units in a neural network receive input, process it, and send output to the next layer.
• Layers: Neural networks are made up of several layers:
  • Input Layer: This is where data enters the network.
  • Hidden Layers: These layers process the data by applying mathematical functions to identify patterns.
  • Output Layer: This layer produces the final result or prediction.
• Weights: Weights control the strength of the connection between nodes, helping the network decide how much importance to give each input.

Now, let’s explore the different types of neural networks — each designed for specific tasks and applications.

Types of Neural Networks

Neural networks have three most common types:

• Feedforward Neural Networks: The most straightforward type, where data moves in one direction from input to output. Commonly used in image recognition.
• Convolutional Neural Networks (CNNs): Primarily used for image and video analysis, CNNs detect patterns and features in visual data.
• Recurrent Neural Networks (RNNs): This is ideal for sequential data, like time series or language processing, as they retain information from previous steps.

Also Read: Recurrent Neural Network in Python: Ultimate Guide for Beginners

Now, it's time to break down the key differences between machine learning vs neural networks and see how they complement each other in modern AI.

Machine Learning vs Neural Networks

While both fall under the umbrella of artificial intelligence, they have distinct characteristics and serve different roles. To clarify this comparison, let’s break them down into specific aspects so you can easily see where each one excels.

It's time to dive into the debate on machine learning vs neural networks.

Now that you know the differences, it’s clear that machine learning vs neural networks aren't competitors but partners in solving AI challenges.

So, it’s time to explore how neural network in machine learning work together.

How Are Neural Networks Used in Machine Learning?

In the vast world of machine learning, neural networks play a transformative role. While ML can operate with traditional algorithms, integrating neural networks — especially in deep learning — opens doors to tasks that once seemed impossible. 

But what exactly is the role of neural network in machine learning models? Keep reading!

What Role Do Neural Networks Play in Machine Learning?

Neural networks are an advanced subset of machine learning, often at the heart of deep learning models. While traditional ML algorithms rely on statistical methods, neural networks mimic how our brains process information.

• Deep Learning: This refers to using deep neural networks with many hidden layers, allowing machines to make decisions based on complex features from data.
• Complex Data: Neural networks are particularly adept at processing unstructured data, like images, videos, and text, where traditional machine learning may fall short.

But how do neural networks enhance machine learning? Read ahead!

Also Read: Deep Learning vs Neural Networks: Difference Between Deep Learning and Neural Networks

Enhancing Machine Learning with Neural Networks

In essence, neural networks bring machine learning to life, enabling it to handle more sophisticated tasks that require high levels of abstraction. Its deep architecture enables models to process and learn from vast data, refining predictions over time.

• Accuracy Boost: Neural networks can model highly non-linear relationships, increasing prediction accuracy.
• Automatic Feature Extraction: They automatically extract features from raw data, reducing the need for manual data preprocessing.
• Scalability: Neural networks perform exceptionally well with large-scale datasets, which is a limitation for many traditional ML algorithms.

By incorporating neural network in machine learning, you get the power of structured learning and the ability to handle massive, complex datasets.

Getting Started with Neural Networks in Machine Learning

Diving into this field might feel like stepping into uncharted territory at first. After all, it’s not every day you get to work with the technology powering self-driving cars and voice assistants. But here’s the good news: with the right tools, guidance, and a bit of curiosity, you'll go from beginner to confident practitioner faster than you think. 

So, how do you get started? Let’s break it down into actionable steps.

Python for Neural Networks

One of the best ways to start with neural networks is by learning Python, the language of choice for building ML models.

• TensorFlow: Developed by Google, TensorFlow is a powerful open-source library for building deep learning models. 
• PyTorch: PyTorch is widely used in academia and research, offering dynamic computation graphs.
• Keras: Keras simplifies building neural networks, making it perfect for beginners.

Also Read: Introduction to Deep Learning & Neural Networks with Keras

These tools allow you to implement neural networks and experiment with real-world datasets.

Online Courses and Certifications

Learning neural networks isn't just about coding but also understanding their theory. upGrad provides a comprehensive curriculum and free courses to deepen your expertise in this field. 

Have a look at some of the top ones:

• Fundamentals of Deep Learning and Neural Networks 
• Learn Basic Python Programming 
• Artificial Intelligence in the Real World

So, ready to take the leap? Start today, book a career counseling session, and unlock the power of neural networks in machine learning!

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