All the buzz around the whole artificial intelligence and deep learning, is leading to more and more confusion. Each of these two concepts has numerous subsets and types that make the understanding even more complex. We thought that it would be a good idea to take baby steps to understanding them and their types. Therefore, the topic for today’s discussion is neural network vs machine learning.
The objective is to demystify the terms and in fact, understand how they are different in terms of skills and application. It goes without saying that neural network vs machine learning is something that almost everyone tries to understand, but, very few actually get it.
Neural Network vs Machine Learning: Demystifying the Terms
Before we move on, let’s have a look at the technical definitions of the terms to better understand what neural network vs machine learning has in scope-
According to Forbes, “Machine learning (ML) is an application of AI (artificial intelligence) that allows systems to learn and improve without being directly programmed.”
Simply put, machine learning is a subset of artificial intelligence. It focuses on learning from data to identify and recognize patterns.
According to Forbes, “An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner.”
Neural network, on the other hand, attempt to model data for machine learning by behaving as interconnected brain cells.
What Skills do you Need?
As we delve deeper into the neural network vs machine learning debate, it is a good idea to discuss the skills that one needs to hone for proficiency.
Neural networks require similar skills when it comes to programming as machine learning. However, when it comes to mathematical skills, the list is a little more comprehensive. Not only do neural network developers need to have a fair understanding of probability and statistics, they also need proficiency in algebra and graph theory. In addition, understanding of data modeling and data structures is a cherry on the cake.
When to Use?
While it is important to understand the theoretical differences and skillsets for neural network vs machine learning, it is also important to focus on their specific applications.
Machine learning, to begin with, has applications in a wide range of industries, including
- Financial Services
- Marketing and Sales
- Online recommendations
Neural network, on the other hand, has its unique set of applications, including-
- Image processing
- Character recognition
- Market forecasting
- Customer research
- Data validation
Examples & Use Cases
Before we conclude our discussion on neural network vs machine learning, it is wise to look at some of the industry giants using these technologies.
Machine learning is popular among most new age organizations. Pinterest, for instance, uses machine learning to improve its content discovery. Similarly, Twitter’s machine learning algorithm curates timelines for users based on individual preferences. IBM’s Watson is being used in several hospitals for better healthcare.
Similarly, the use of neural networks is also widespread. Firstly, LinkedIn uses neural networks to detect spam and abusive content that comes on its platform. Similarly, DialogTech uses neural network as a means to classify and categorize inbound calls.
To cut a long story short, neural network vs machine learning is a never ending debate. Each comes with its own strengths and weakness. Follow the above data to make an informed choice about what suits your needs the best. If you wish to boost your technology stand with neural network or machine learning, write to us today! We offer state of the art services when it comes to machine learning and neural networks.