Machine Learning vs Neural Networks: Understanding the Key Differences

Machine learning vs neural networks often sparks confusion, but they represent distinct concepts in the field of artificial intelligence. Machine learning is a broader field that includes various algorithms for data analysis and prediction, whereas neural networks are a specific subset of machine learning models designed to mimic the human brain's structure and functioning.

In this blog, we’ll explore the key differences between machine learning vs neural networks, their unique features, and where each excels. By the end, you'll gain a clear understanding of which approach is best suited for different applications in the AI space.

What is Machine Learning? Key Concepts and Applications Explained

Simply put, machine learning is a branch of Artificial Intelligence 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.

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Also read: Machine Learning Tutorial: Learn ML from Scratch

Exploring the Different Types of Machine Learning

Machine learning isn't just a single approach, it's a versatile field that encompasses various methods tailored to different types of data and problem-solving scenarios. The three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning, each with its own applications, advantages, and use cases.

• Supervised Learning

Supervised learning is one of the most commonly used machine learning techniques, where the model is trained on labeled data. In this type, the algorithm learns from input-output pairs, where the "output" or label is already known. The model’s goal is to make predictions or classify data based on this pre-labeled data. After training on the dataset, it can generalize to new, unseen data.
Example: Predicting house prices. Algorithms like Random Forest Regressors or Linear Regression are used to predict house prices. Features such as size, location, and number of bedrooms train the model to estimate the price. For instance, using Random Forests, the model can handle complex, non-linear relationships, often achieving an R-squared value above 0.85, indicating high predictive accuracy in real estate price forecasting.

• Unsupervised Learning

Unlike supervised learning, unsupervised learning works with unlabeled data. The model’s task is to uncover hidden patterns, groupings, or structures in the data without any predefined labels. It’s particularly useful when we don't have explicit outcomes or labels and instead need to find patterns within the data itself. This type of learning is crucial for exploring complex datasets where the target or goal isn't initially clear.
Example: Customer segmentation. Algorithms like K-means clustering segment customers based on behaviors such as purchasing habits and demographics. For example, e-commerce businesses use unsupervised learning to identify customer segments like "frequent buyers" or "price-sensitive shoppers." K-means clustering minimizes intra-cluster variance and improves targeted marketing efforts, potentially increasing campaign ROI by 20%.

• Reinforcement Learning 

Reinforcement learning (RL) is unique in that it mimics human-like learning, where agents (models) interact with their environment and learn to maximize rewards over time. The agent takes actions and receives feedback in the form of rewards or penalties, using this information to adjust future behavior. The goal of the model is to explore different actions and gradually improve to achieve the best long-term results.
Example: A robot navigating a maze. Algorithms such as Q-learning or Deep Q-Networks (DQN) help robots or autonomous systems learn from their environment. In a maze, a robot is rewarded for reaching the goal and penalized for wrong turns. DQN, which combines deep learning with RL, is used in applications like autonomous vehicle navigation, improving decision-making by adapting to real-time feedback from the environment.

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

Real-World Applications of Machine Learning

Machine learning is a powerful tool that is increasingly integrated into everyday life and various industries. Here are some prominent examples of its widespread applications:

• Recommendation Systems: Platforms like Netflix and Amazon leverage machine learning to analyze user behavior and preferences, offering tailored recommendations for movies, products, or shows based on past interactions.
• Fraud Detection: Financial institutions use ML algorithms to spot irregular patterns in transactions, identifying potential fraudulent activities and preventing financial losses.
• Autonomous Vehicles: Self-driving cars utilize ML to process sensor data, interpret their surroundings, and make split-second decisions to navigate roads safely without human intervention.
• Healthcare: Machine learning is transforming healthcare by assisting in diagnosing diseases, predicting patient outcomes, and providing personalized treatment plans based on individual data.

What ties these applications together is their reliance on pattern recognition and real-time decision-making. Whether recommending products, detecting fraud, driving cars, or diagnosing diseases, machine learning systems continuously analyze vast amounts of data, identify patterns, and make informed decisions on the fly. 

These capabilities not only optimize operational efficiency but also contribute to advancements that improve quality of life across various sectors.

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

What is a Neural Network? Understanding Components & Types

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. 

Key Components of Neural Networks

Neural networks are the backbone of deep learning, modeled after the structure of the human brain. These networks consist of several key components that allow them to process information and learn from data. Here’s a more detailed breakdown:

• Nodes (Neurons): These are the fundamental units of a neural network, analogous to neurons in the human brain. Each node receives input from the previous layer, processes it, and sends an output to the next layer. The output is determined by applying an activation function to the weighted sum of the inputs.
• Layers: Neural networks are composed of multiple layers, each with a specific role in processing data.
  • Input Layer: This is where data enters the network. Each input neuron corresponds to one feature of the input data.
  • Hidden Layers: These are intermediate layers that process the data by applying mathematical functions to detect patterns. Each neuron in a hidden layer takes inputs from the previous layer, applies weights and an activation function, and passes the output to the next layer. The number of hidden layers can vary, and deeper networks with more hidden layers can capture more complex patterns.
  • Output Layer: This final layer produces the prediction or result, representing the output of the network based on the learned features from the input data.
• Weights: Weights are the parameters that control the strength of connections between nodes. Each connection has a weight that adjusts as the network learns, influencing the importance given to each input. The network learns by adjusting these weights during training to minimize the error in its predictions.

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

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

Different Types of Neural Networks and Their Uses

Neural networks come in various forms, each optimized for specific types of data and tasks. Below are the three most common types:

• Feedforward Neural Networks: Feedforward Neural Networks are the simplest and most commonly used type. In this network, data flows in one direction—from the input layer, through any hidden layers, and finally to the output layer. There are no cycles or loops, making it ideal for tasks like image recognition or simple classification problems. 

However, feedforward networks struggle with sequential or spatial data, which is why more specialized networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) were developed to handle those complexities.

• Convolutional Neural Networks (CNNs): CNNs are specialized neural networks designed for processing grid-like data, such as images or videos. They use a convolutional operation to automatically detect features like edges, textures, and shapes in visual data. CNNs are widely used in computer vision tasks, such as object detection, facial recognition, and medical imaging analysis, due to their ability to extract hierarchical patterns from images.

However, CNNs require large labeled datasets and high compute resources for effective training, which may not be feasible in all contexts, especially for smaller organizations or projects with limited data and computational power.

• Recurrent Neural Networks (RNNs): RNNs are ideal for sequential data, as they have connections that form cycles, allowing information to persist. This ability to "remember" previous information makes RNNs effective for tasks such as time-series forecasting, speech recognition, and language modeling. RNNs are used in applications like text generation and machine translation, where understanding context from earlier steps in the sequence is crucial.

When deciding which type of neural network to use, Feedforward Neural Networks (FFNN) are best for static input, Convolutional Neural Networks (CNNs) excel with spatial data like images, and Recurrent Neural Networks (RNNs) are ideal for sequential or temporal data.

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 have become a cornerstone in modern machine learning, significantly enhancing its capabilities. While traditional machine learning algorithms work with preprocessed, structured data and rely on statistical methods, neural networks bring a more powerful and flexible approach, particularly suited for deep learning tasks.

For example, in fraud detection, traditional machine learning might require feature engineering, where specific characteristics like transaction frequency or geographical location must be manually selected as features for the model. In contrast, neural networks can automatically learn complex patterns directly from raw data without the need for manual feature selection, making the process much more efficient and adaptable to new data.

These networks are inspired by the human brain, mimicking the way neurons process and transmit information, making them ideal for handling complex, unstructured data.

Also read: Structured Vs. Unstructured Data in Machine Learning

• Deep Learning and Neural Networks

Deep learning is a subset of machine learning that leverages deep neural networks, which consist of multiple hidden layers, to model highly complex patterns in data. The term "deep" refers to the number of hidden layers in the network—more layers allow the model to learn increasingly abstract features of the data, increasing its ability to capture complex relationships. 

These deep neural networks enable the machine to "learn" directly from the data, rather than relying on predefined feature extraction. This allows deep learning models to perform tasks such as speech recognition, image classification, and natural language processing, which were once thought to be beyond the capabilities of traditional machine learning.

• Neural Networks and Complex Data

Neural networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are especially effective at processing unstructured data like images, videos, and text. These networks excel at tasks that traditional machine learning models struggle with, offering significant improvements in accuracy and scalability.

CNNs, for example, automatically detect complex patterns in visual data such as edges, textures, and shapes, enabling superior performance in image and video analysis. RNNs, on the other hand, excel at handling sequential data, such as text or time series, where the context from previous steps plays a crucial role in decision-making. The ability of these networks to learn from raw, unstructured data without the need for manual feature engineering allows them to outperform traditional machine learning models in both precision and the ability to scale across large datasets.

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

But how do neural networks enhance machine learning? Let's have a look.

How to Enhance 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, such as recognizing emotional tone in speech or understanding the context of an image. Its deep architecture enables models to process and learn from vast data, refining predictions over time.

• Accuracy Boost:

Neural networks are capable of modeling highly non-linear relationships within data, allowing them to capture complex patterns that traditional machine learning algorithms often miss. For example, CNNs outperform logistic regression in image classification tasks by learning hierarchical features, such as edges, textures, and objects. 
In benchmarks like MNIST or ImageNet, CNNs achieve significantly higher accuracy than simpler models, demonstrating their superior ability to learn from intricate features and interactions between data points. This enhances prediction accuracy, particularly in tasks like image recognition and natural language processing.   

• Automatic Feature Extraction:

One of the key advantages of neural networks is their ability to automatically extract relevant features from raw data. This eliminates the need for time-consuming manual feature engineering, allowing models to work directly with unstructured data, such as images, text, and audio. In CNNs, for example, early layers learn edges and textures, while deeper layers learn more complex features like objects. The network learns these important features during training, which improves model efficiency and reduces human intervention. 

• Scalability:

Neural networks are designed to scale effectively with large datasets, making them ideal for big data applications. Traditional machine learning algorithms often struggle to maintain performance as dataset sizes grow. In contrast, neural networks, especially deep learning models, excel with larger volumes of data. These models frequently outperform conventional algorithms in tasks like speech recognition and large-scale image classification.

However, this scalability comes at a cost, neural networks require more compute and memory than traditional models.   

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. 

What You Should Know First:

Before diving into the tools, it's helpful to have a basic understanding of a few key concepts:

• Linear Algebra: Essential for understanding how algorithms work under the hood.
• Python Basics: As the most popular language for machine learning, knowing Python is a must.
• Supervised Learning: A foundational concept in machine learning that involves training a model on labeled data.

Once you're comfortable with these concepts, you'll be well-equipped to explore the tools and techniques that can help you make an impact in the world of AI.

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

Using Python for Building Neural Networks

Python is widely considered the best language for building neural network models, thanks to its simplicity and rich ecosystem of libraries. Among the most popular tools for deep learning are:

• TensorFlow: Developed by Google, TensorFlow is an open-source library designed for building complex deep learning models. It supports both training and inference across a range of platforms, from mobile to desktop.
• PyTorch: Often preferred in academic research, PyTorch provides dynamic computation graphs, offering flexibility for experiments. It’s known for its ease of use and integration with Python.
• Keras: Keras is a high-level neural network API that simplifies building and training deep learning models. It acts as an interface to TensorFlow and is ideal for beginners due to its simple syntax and high-level functionality.

Which one to start with?

If you’re just starting, Keras is a great choice for quick wins due to its simplicity. Once you’re comfortable with deep learning basics, you can gradually transition to TensorFlow or PyTorch for more control and flexibility, especially for complex models and research purposes.

First Project Idea:

To get hands-on experience, try building a handwritten digit recognizer using the MNIST dataset with Keras. It’s a straightforward project that will help you apply the concepts you’ve learned and kickstart your journey in deep learning.

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

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

Build Expertise in Machine Learning and Neural Networks with upGrad!

The debate between machine learning vs. neural networks comes down to the nature of the data and the task at hand. While machine learning offers robust solutions for structured data and simpler tasks, neural networks excel in handling unstructured data and complex problem-solving. 

By diving deep into neural networks, you can use powerful tools that bring out the best in your data. To master these technologies, upGrad’s advanced programs offer the skills and expertise to help you excel in the world of machine learning and neural networks.

Have a look at some of the top ones:

• Fundamentals of Deep Learning and Neural Networks 
• Learn Basic Python Programming 

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

For personalized guidance, schedule a session with our experts at upGrad Counselling or visit one of our offline centres for a more hands-on experience.

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