Machine Learning vs. Neural Networks: What’s the Difference?

Written by Coursera Staff • Updated on

Learn about the differences between machine learning vs. neural networks, as well as relevant careers in these fields.

[Featured Image] A group of students sit at laptops during a seminar and discuss differences between machine learning vs. neural networks.

Machine learning and neural networks both play a role in artificial intelligence. Machine learning is a subset of artificial intelligence, while neural networks are a subset of machine learning. Advancements in neural networks have led to the introduction of new machine learning models, such as deep learning. Deep learning utilizes layers of neural networks to extract insights from significant amounts of data. Machine learning and neural networks play a role in several industries today, including finance, health care, marketing, transportation, manufacturing, and robotics.

Machine learning

Machine learning models are made up of algorithms that are capable of recognizing patterns within data. You can use the insights provided by machine learning models to make predictions and inform business decisions. Machine learning models improve over time as they learn from data independently, without the need for specific instructions from a programmer, similar to how humans can learn over time.

Machine learning begins with training data or input data. By analyzing this information, machine learning models can identify patterns and trends in the data to make accurate predictions for the future based on what happened in the past. Since this occurs autonomously, it allows processes to run much more efficiently. Neural networks are one machine learning algorithm. Other common algorithms include linear and logistic regression, decision trees, clustering, and classification.

Types of machine learning methods

Machine learning algorithms typically fall into one of three categories: supervised learning, unsupervised learning, or reinforcement learning. 

Supervised learning

The training data features manually labeled input and output data in supervised learning. By giving the model the expected outcome, it learns how to make connections between the data to make predictions on its own with future tasks. Examples of machine learning algorithms that use supervised learning are neural networks, linear regression, and logistic regression.

Unsupervised learning

Rather than giving the model labeled input and output data, in unsupervised learning, only the input data has labels, and the model develops the output independently. Although mistakes are more common in unsupervised learning, it’s useful when the desired results or outcomes are not yet determined. Neural networks use unsupervised learning, as do clustering methods. Additionally, unsupervised learning is great for exploratory data analysis, where the algorithm can identify unknown patterns. 

Reinforcement learning

Reinforcement learning, or semi-supervised learning uses both a labeled and unlabeled data set, where only sometimes the model receives an output. With reinforcement learning, the goal is to train the model through trial and error to understand when it’s correct so it knows how to operate moving forward. Neural networks sometimes use reinforcement learning, as do self-driving cars and video games. 

Neural networks

Neural networks are a specific type or subset of machine learning in which the model has interconnected nodes that function similarly to those of a human brain. This allows the model to learn and identify the relationships found between input and output data. Neural networks also undergo supervised, unsupervised, or reinforced training. With neural networks, you can develop predictions, classify data into predefined or unique classes, and identify patterns.

Neural networks have three layers of interconnected nodes; the input layer, the hidden layer, and the output layer. Depending on the type of neural network, there can be several hidden layers within the hidden layer. Neural networks receive data through the input layer, where it’s categorized before moving to the hidden layer for processing. The data goes through further processing at each hidden layer until finally reaching the output layer, where the model produces an output. The output layer produces one or more outputs depending on the number of output nodes it has.

Neural networks have applications in a number of industries. Here are some of the ways you can use neural networks:

  • Face, fingerprint, and speech recognition

  • Product analysis and design

  • Fraud detection

  • Medical diagnosis

  • Stock market predictions

  • Targeted marketing

Types of neural networks

Common types of neural networks include convolutional neural networks, feedforward neural networks, modular neural networks, and recurrent neural networks.

Convolutional neural networks

Convolutional neural networks (CNNs) are neural networks largely implemented for analyzing images. CNNs have multiple layers, including the convolutional layer and pooling layer. Each hidden layer in the convolutional neural network has a unique role in processing features of the image.

Feedforward neural networks

Feedforward neural networks (FNNs) follow the basic neural network structure of an input layer, hidden layer, and output layer. They have applications in natural language processing and computer vision. Each node in a feedforward neural network connects directly to a node in the next layer.

Modular neural networks

Modular neural networks have independent modules where each module performs a specific task within the neural network. This allows modular neural networks to have flexibility, simplify problems, and handle any bugs that arise more efficiently.

Recurrent neural networks

In a recurrent neural network, you can use outputs again as inputs. This feedback loop allows recurrent neural networks to recall previous data when processing similar data to make better predictions. Recurrent neural networks are ideal for areas such as making predictions about the stock market or text suggestions when typing out a message. 

Machine learning vs. neural networks: Pros and cons

Machine learning and neural networks come with both advantages and disadvantages. Machine learning and neural networks enable you to analyze massive amounts of complex data. These algorithms and models also allow you to develop valuable insights with minimal human intervention, as they can learn independently. You can also simplify your data storage since you can store the data directly within the network.

When comparing neural networks to more traditional machine learning methods, training neural networks is far more time-consuming. In addition to requiring more time, training neural networks tend to require more data. Another concern in machine learning is data privacy and security. Since you may be handling people's personal information, you have an obligation to take extra measures to ensure it remains secure by implementing proper storage and encryption strategies.

Careers in machine learning and neural networks

Pursuing a career in this space requires a number of technical skills. You should develop your programming skills and have knowledge in math and statistics, data science, and artificial intelligence. Along with these technical skills, important workplace skills include organization, communication, and teamwork. Here’s a look at some of the job titles you can pursue where you can use machine learning and neural networks:

  • Deep learning engineer

  • Machine learning engineer

  • Data engineer

  • Data scientist

  • Software engineer

  • Business intelligence developer

Getting started with Coursera

On Coursera, you can find highly rated courses to learn more about machine learning and neural networks. Introduction to Machine Learning from Duke University can help you develop your knowledge of several machine learning models, including logistic regression and convolutional neural networks.

You can also learn more about neural networks from Neural Networks and Deep Learning from DeepLearning.AI. This course covers the basics of neural networks and can help you develop deep learning and Python programming skills.

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