Machine Learning vs Neural Networks

Neural networks

‘Neural Networks’, as the name suggests, are inspired by the human brain. Just like how neurons in our brain signal one another, a neural network consists of nodes containing particular information. Neural networks are the subset of machine learning that process complex data and are the heart of deep learning algorithms.

You may think of ‘neural networks’ as interconnected units that work together to solve a problem, where each small unit (node) contains information, and that particular information is passed on to the other unit. They accept vast amounts of data that could be text, images, etc., and further analyse it to recognise patterns and then make predictions accordingly. They are dependent on data for improving accuracy.

Applications of Neural Networks

There are many applications of neural networks, such as:

• Facial recognition 
  Neural networks are used in face recognition systems for analysing facial features and identifying a person. They are used in workplaces, mobile phones, etc, for security purposes.
   
• Stock Market Predictions
  Neural networks are used to predict future possibilities in the stock market. ‘Long Short-Term Memory’ and ‘Recurrent Neural Networks’ are widely used in stock market predictions.
   
• Aerospace
  In aerospace, ‘time delay neural networks’ are used for recognising features regardless of their spatial position. 
   
• Defence
  In Defence, ‘convolutional neural networks’ are used to know whether any ‘mine’ is present underwater. Neural networks are used in arm attack analysis, air patrols, maritime patrols, and object locating.

Future and Scope of Neural Networks

Neural Networks will continue to prevail in the digital space. Researchers have come a long way from building robots to self-driving cars, and still, there is much more to explore and revolutionise. 

Self-driving cars like Tesla use neural networks to determine a vehicle's surroundings so that the car can plan the path and navigate accordingly. 

From banking to doctor visits and defence to aerospace, neural networks have made things much more manageable.

Usage of Neural Networks in various career paths

Neural networks are used in various career paths, such as:
 

• Test Engineers: Test engineers use neural networks for building an automated testing system. 
   
• Research Scientist: Scientists use neural networks to analyse their data and for prediction analysis. Neural networks also help in improving the essential functions of a particular task.
   
• Applied Scientist: Applied scientists prefer using neural networks to help businesses improve their business outcomes. Neural networks help in enhancing customer satisfaction, thus increasing output. They are also used in fraud detection.
   
• Data Engineer: Data engineers use neural networks to build models for data extraction.
   
• Software Engineer: Software engineers prefer using neural networks to develop models that can handle vast amounts of data and perform the necessary functions. 
   

Recommended read- Artificial Neural Networks - ANN and Ensemble Learning

Machine Learning vs Neural Networks

Frequently asked questions

What is Machine learning?

Machine Learning is a subdomain of AI (artificial intelligence) that involves developing models that can automate tasks and learn from experiences without any human intervention.

What are Neural Networks?

Neural networks are the subset of machine learning that process complex data inspired by the human brain. In neutral networks ‘backpropagation’ process is used for learning purposes.

What is the use of Neural Networks?

Neural Networks are used in healthcare, speech and image recognition, and automatic vehicles.

What is the use of Machine learning?

Machine learning is used in Image recognition, recommendation systems, predictions, NLP, etc.

What is the difference between Machine Learning and Neural Networks?

Data analysis and past experiences form the basis of the decision in machine learning. On the other hand, the decisions in Neural networks are made by arranging the algorithms such that the models can make decisions more reliably.

Conclusion

In the article, ‘machine learning vs neural networks’, we have discussed some of the key differences between them. We have discussed how decisions are made in both of them and discussed their difference in tabular form. To enhance your knowledge about machine learning and neural networks, refer to the articles below.

• Start Learning Full-Stack Web Development For Free
• Machine Learning with Python
• 12 Most Used Machine Learning Algorithms in Python 
• Visualizing Convolutional Neural Networks with Filters
• Noise Injection in Neural Networks 
• Intro to Feedforward Neural Networks 
   

You can also consider our Machine Learning Course to give your career an edge over others.

Happy coding!