Neural Network vs Machine Learning

Neural Network vs Machine Learning is a topic of interest for AI enthusiasts. Artificial Intelligence is taking its toll over much of the processes humans usually performed not very long ago. There is no doubt that AI is now at the forefront and can be spotted in every field and every industrial sector including medicine, space technology, security systems, production, and quality assurance.

But how exactly Artificial Intelligence has achieved this feat and what really makes it a cutting-edge technology? AI is considered the third most dramatic technology humans have ever invented after Fire & Wheel that has changed the course of history for human civilization 1. But the big question is, how exactly all this happens, and what are the core aspects of AI?

Here we will look at two of the most important hands of AI that make things possible and make a comparative analysis of both the sub-systems of the mighty Artificial Intelligence. Machine Learning and Neural Networks, two of the most prized assets of AI. Further, into the article we will differentiate both of these sub-systems on the basis of their utility, usability, functionality as well as key use-cases and see which of these sub-systems is more effective, result-oriented, and technically reliable 2.

So, let us go on with our comparison we call “Neural Networks vs Machine Learning”.

Neural Network vs Machine Learning: The Basic Difference

Before going in deeper its imperative to understand both on a basic level, in a layman’s view, and get to know them better. Neural Networks and Machine Learning, are imitations of two of the main human cognitive operations namely pattern recognition and Neural Activity 3. If we look at the human brain and its functionality, the neurons fire in different combinations resulting in different perceptions and ultimately different perceptions that depend on patterns’ recognition.

Let us put things into an Artificially Intelligent perspective, Machine Learning is a system that mainly relies on developing the patterns from the data it is fed. Of course, these patterns are developed through various types of models which are primarily categorized as supervised, unsupervised, and semi-supervised models to recognize the patterns. Neural Network, on the other hand, is more complex in nature and is a combination of various nods and layers and doesn’t need any supervision at all 4.

Neural Network vs Machine Learning: The Human Connection

Machine Learning needs more human intervention than Neural Networks. Let us put it this way, for example, you are to decide between two different tasks that are either to buy milk first or visit a petrol station first. The first thing you will do is to check the petrol gauge, then you will measure the distance you need to travel and which direction you should move that should get you closer to your destination, you could get your petrol tank filled as well as buy milk as well.

In short you wish to save time and fuel (resources) but also wish to complete both tasks. Suddenly pops up this petrol station with a wonderful utility store as well, that too on the way home and viola! You achieved all three of your major objectives. Now, the more you have been in this situation before the better your mind works given your “experience” in the same situation by recognizing patterns on past “data” (experiences).

Machine Learning does the same, all it needs is your data (previous experiences, the more the better), identify the features (Fuel, Time) and the objectives (milk, refiling) and through a valid algorithm will develop a model and make a prediction about the right petrol station or the right direction where all your goals are met.

On the other hand, Neural Networks are more objective in nature and solve a different class of problem for us. These networks are more concerned about the way a decision is taken than the decision itself. Neural Networks need least human intervention and develop a sense of situation on their own and through trial and error, learn from their mistakes develop a mechanism to make sense of the whole scenario.

Neural Network vs Machine Learning: Deployment and Usability

Well Neural Networks vs Machine Learning is now going to get real as we look in to the comparison through the technical lens. The most important thing is to understand that both these sub-systems are used to address different problems on the basis of scope, specialty and complexity. Neural Networks are deployed in more complex situations like, image recognition, sentiment analysis, speech recognition, text to speech, computer vision, cybersecurity and the likes, all based on audio and visual data 5.

Machine Learning on the other hand is used for more quantitative analysis and is a base for analytics in any domain, as it thrives on the big-data and more so the quality and relevance of the data as well. It is used to solve less complex and more stable situations which involve lots of statistical attributes. Furthermore, Machine Learning is more concerned with the real time data generated through IoT devices and is consistent with its model unless tweaked 6.

Machine learning models are deployed in Health Care, E-Commerce, Retail, Online Recommendations, Price variation tracking, Enhanced customer services and delivery systems. While Neural Networks are deployed in various sectors like Finance, Stock Exchange Prediction, Sales Forecasting, risk management, data validation and the likes.

Neural Network vs Machine Learning: The Required Skills Set

One major point of differentiation between Neural Networks and Machine Learning is the set of skills. Since both sub-systems address different problems thus there are some major skills specific to each and for any individual who wishes to make career in any of these, must be aware of. Some common skills include programming, Probability and Statistics, Data Structures and Algorithms

The specialized skills for Machine Learning are Big-Data or Hadoop, understanding of machine learning frameworks. For Neural Networks the skills include Mathematics, Linear Algebra, Graph Theory, Data Modeling. For every ML or NN professional it is imperative to learn the proper skills set and in addition to the above mentioned hard-skills it is equally important to get proficient in soft-skills like critical thinking, emotional intelligence, creativity and innovation 7.

Neural Network vs Machine Learning: The Use-Cases for both ML & NN

Some of the use-cases for Neural Networks are;

  • UNTAPT is using Neural Networks to improve their Human resource functions by identifying most relevant roles for an individual as well as making the hiring procedure more efficient and ultimately improving the performance. They used 16-layer neural networks through millions of data points to achieve this magnificent feat.
  • OKRA Technologies deployed Neural Networks to find the most suitable parents for the foster children to ensure maximum stability.
  • Talla a well-known “digital workers” solutions provider has developed an AI system for salesmen through Neural Networks so that the relevant product details furnish before them as soon as they take up a call with the customers’ inquiries.
  • Twitter uses Neural Networks to identify fake information and fake accounts as well to ensure that the information displayed on their networks is not only true but also from the correct source. Almost all the big names such as politicians, celebrities, sports professionals use their platform and ensuring the quality of information is a big challenge they have addressed to a credible extent.

Some of the use-cases for Neural Networks;

  • Netflix uses, “Recommender-Systems” a very well-known algorithm in Machine Learning. This is how Netflix determines the profile of their viewers and recommends them the next season or movie to watch based on their preferences.
  • Amazon also uses the same “Recommender-Systems” to sell its products and specifically the books. As the recommender system determines that anyone who buys product A, usually buys product B as well. Thus, more targeted marketing ensures the number of sales soaring for them.
  • Facebook uses its unique AI system called “FBLearner Flow” which is based on machine learning to identify text, likes and shares and is based on the information provided by the users themselves.

Neural Network vs Machine Learning: The Final word

Neural Networks and Artificial Intelligence both have equal importance in this age of information overload. The biggest challenge these systems are facing is the quality as well as relevance of the data they are fed with. Neural Networks take some lead in certain cases as they need far less human intervention and can work on their models on their own. Yet, there are a lot of endeavors where Machine learning holds the torch.

Especially where data ais involved, and not just the data, we mean a lot of data and that too in continuous form. Take the example of facebook, there are millions of posts each second and the ML systems are getting on with them. If you wish to choose between Neural Networks and Machine Learning systems as a career, remember you must know how do you approach the situation? If subjectively, then go for ML and if objectively, then of course you are the right choice for NN.  I will keep coming with much more on AI and its sub-systems, don’t forget to leave a comment and share this within your network too.


  1. Harari, Yuval Noah. Homo Deus: A brief history of tomorrow. Random House, 2016.
  2. Mitchell, R. S., J. G. Michalski, and T. M. Carbonell. An artificial intelligence approach. Berlin: Springer, 2013.
  3. Lu, Huimin, et al. “Brain intelligence: go beyond artificial intelligence.” Mobile Networks and Applications 23.2 (2018): 368-375.
  4. Van Gerven, Marcel, and Sander Bohte. “Artificial neural networks as models of neural information processing.” Frontiers in Computational Neuroscience 11 (2017): 114.
  5. Aggarwal, Charu C. Neural networks and deep learning. Springer,, 2018.
  6. Oussous, Ahmed, et al. “Big Data technologies: A survey.” Journal of King Saud University-Computer and Information Sciences 30.4 (2018): 431-448.
  7. Tegmark, Max. Life 3.0: Being human in the age of artificial intelligence. Knopf, 2017.

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