When it comes to the vast field of Artificial Intelligence (AI), it can become a little tricky to differentiate between different terms. The same thing happens with Neural Networks and Machine Learning. Many people believe they are the same, while they are not. So, in this article, we are going to cover a comprehensive Neural Networks vs Machine Learning comparison to point out the key differences between them. Hopefully, this article will make it easier for you to differentiate between the two on top of enabling you to decide when to use Neural Networks and when to use Machine Learning algorithms. So, without further ado, let’s dive into it!
What Is Machine Learning?
Machine Learning is a field in AI that involves both computers and statistics. Basically, it uses data collections to learn and execute tasks. The algorithms involved in Machine Learning enable the AI to independently make decisions based on the statistical data it is being fed. This means that a Machine Learning program will not need to be programmed for each and every possible situation. The two broad types of Machine Learning are supervised and unsupervised learning. Machine Learning programs can be extremely simple or extremely complex, and their reliability will depend on the quality of coding as well as the data sets fed to them.
What Are Neural Networks?
Basically, Neural Networks are a part of Machine Learning. They are typically used in unsupervised Machine Learning. Neural Networks in Machine Learning are structured like Neural Networks in our brain (that’s the reason they share the same name). There are different layers in Neural Networks, and data is carried in these layers. These layers include input layers, some hidden layers, and an output layer. Neural networks are used to classify and categorize different types of data. You can feed training data into Neural Networks to increase their accuracy.
Neural Networks vs Machine Learning: The Key Differences
The question about the difference between Machine Learning and Neural Networks is an understandable one. The primary and key difference between Machine Learning and Neural Networks is the fact that one is a subset of the other. Now that we know what causes this confusion between Machine Learning and Neural Networks, let’s compare them and analyze how they are different from each other in terms of functions, applications, etc.
The first and the main difference between Machine Learning and Neural Networks lies in the algorithm they use. Machine learning algorithms have a relatively simple arrangement. Decision trees are one example of this. These algorithms are fed data that they study and make decisions accordingly. On the other hand, the algorithms in Neural Networks consist of layers, and they are much more complex.
Since Neural Networks are a part of Machine Learning, they use an array of algorithms that are also used in Machine Learning to study data and make decisions. This assortment of Machine Learning algorithms makes Neuron Networks a much more complex and accurate system.
The amount of supervision needed in Neural Networks vs Machine Learning is completely different. Machine Learning algorithms need to feed on specific data sets to build their knowledge base, and human intervention is required in order to learn the desired procedure to deal with future data. However, this need for human involvement only occurs in the early stages of setting up a Machine Learning system.
In case the Machine Learning program comes up with an inaccurate or undesirable outcome, a Machine Learning engineer will have to step in and fix whatever is causing that error. They might need to feed more data into the program to make it learn more and make more accurate decisions.
On the other hand, Neural Networks don't require human interaction at all. The layers keep passing data to each other automatically according to a hierarchy. This makes the Neural Network system capable enough to recognize and learn from its own errors.
3- Decision Making:
Let's take a look at the Neural Networks vs Machine Learning comparison in terms of decision-making abilities. Machine Learning systems can only make decisions based on the data it has been fed. It will analyze and learn from that data to predict certain outcomes or to give an appropriate response. On the other hand, Neural Networks can independently make decisions. It does not need a stream of data to be fed into it to be able to learn and make decisions. This is all thanks to the complexity of the layers of algorithms that make up a Neural Network.
Machine Learning algorithms can be used to classify data accurately. They can identify types of data and pass on the required types of classified data ahead, or they could decide to withhold a certain type of data for the time being. Considering this, you might be confused about when to use Neural Networks if Machine Learning algorithms are already able to classify data.
Well, the answer is quite interesting. Basically, Neural Networks actually involve multiple layers of Machine Learning algorithms, as mentioned above. So, in Neural Networks, there are numerous Machine Learning algorithms working to classify data and pass on some of it to the next nodes till it reaches the output layer. This makes the classification done by Neural Networks much more accurate.
Let's move forward toward the difference between Machine Learning and Neural Networks in terms of their working. Neural Networks are able to make use of a large amount of complex data. They can detect patterns and find trends that would have been impossible for a human brain to detect. They are perfect for processing various data forms, and their layers ensure high accuracy in the processing of data.
Compared to this, Machine Learning is used for simpler tasks. Simpler data is fed into Machine Learning systems so they can learn and make decisions based on it. Machine Learning does not involve layers or nodes, and that’s why they are only used to predict outcomes and recognize simple trends in data produced.
There are multiple kinds of layers in Neural Networks. For starters, there is an input layer that receives data. Then, there are multiple hidden layers that have their own functions. For example, the pooling layer generalizes data to make it easier to carry out tests. Similarly, the dropout layer destroys random and unnecessary data to prevent overfitting and so on. Finally, the output layer is the final layer which is responsible for producing results based on data that has been processed by the Neural Network. When it comes to Machine Learning, there aren't that many layers involved, which makes the Machine Learning algorithm simpler.
7- Skills Required:
Machine Learning engineers need to have a strong grip on programming as well as statistics and probability. They need to be well acquainted with the methods required for handling big data and computer algorithms. Most importantly, Machine Learning engineers need to know how to read and analyze Machine Learning frameworks.
When it comes to Neural Networks, a different set of skills is required. This includes mathematical knowledge, data modeling skills, and more. Moreover, a Neural Network developer should also be able to work with linear algebra, statistics, and programming. The last three skills are also common in Machine Learning. Since Neural Networks are a subset of Machine Learning, some required skills overlap.
8- Application Area:
To know when to use Neural Networks and when to use Machine Learning, let's look at their applications. There are actually a lot of uses for Machine Learning. It is used in self-driving cars, the healthcare sector, eCommerce, retail, and even in online video streaming applications. Keep in mind that these are just a few examples of the vast list of applications of Machine Learning.
While Machine Learning is often used to replace humans in doing small, tedious tasks, Neural Networks are always used for large tasks. Some examples of the application of Neural Networks include sales forecasting, customer research, speech recognition, data validation, and character recognition. All of these tasks require a large amount of data to be processed.
Machine Learning vs Neural Networks: Which One Should You Use?
If you want to find out whether you should go for Machine Learning or Neural Networks, you should closely analyze the type of scenario you have. This is because both of them will be effective in entirely different situations. For instance, Neural Networks will be most useful in places where multi-dimensional forms of data need to be processed. Also, they will be most efficient with complex and large amounts of data. Most of the time, it'll be clear whether Neural Networks are required for a project. Meanwhile, you can use Machine Learning if you have a scenario where you need quick predictions. The software will learn and predict outcomes based on the latest data.
Machine Learning and Neural Networks are at the forefront of modern technology. Both of these technologies hold a lot of promise, and many businesses are actively working on them. Hopefully, by the end of our Machine Learning vs Neural Networks comparison, you'll have a pretty good idea of which technology will work better for you. If you're still unsure, feel free to reach out to us, we'll be more than happy to help you out.
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