13 Things About Machine Learning vs Deep Learning You May Not Have Known


This article will certainly help you to find out what are Machine Learning and Deep learning along with the key differences between Machine Learning vs Deep Learning

This is one of the common doubts or questions in I guess every Machine Learning Enthusiast or Deep Learning Enthusiast or an Artificial Intelligence (AI) enthusiast. Sometimes it may also happen these terms are misinterpreted and used in wrong places too. These terms are quite usually coined in the wrong way and shown to the audience. So, I think that I may be able to clear your basic doubts about these terms and give you a proper idea of what these terms mean.

Before deep-diving into the concepts and learn about what these terms actually mean and how they work actually, let’s get to know some basic terms first.

First of all, what do you mean by the term Learning?

In simple terms for a human being, we can say that Learning is the process of acquiring new information.

So now the question arises what is Learning for a Computer?

In simple terms, we can say that it is the science of getting computers to learn without being explicitly programmed.

You may have probably used a learning algorithm dozens of times without knowing it.

Let’s get through this with some of the examples:

  • Web Search: Every time you use a website search engine like Google or Bing to search the internet, one of the reasons that it works so well is because of a learning algorithm, like the one that is being used by Google and Microsoft to rank the pages.
  • Facebook: Every time you use a Facebook or Apple’s photo typing application and it recognizes your friend’s photo that’s also a learning algorithm.
  • E-mail Spam: Every time you read your E-mail and your spam filter saves you from having to wade through tons of spam email, that’s also a learning algorithm.

Both of these concepts are a subset of the most common and widely used term that is Artificial Intelligence.

But now what is exactly Artificial Intelligence?

There have been several attempts to define this term but many of them define these terms such that they are not understood easily by a layman. Thus, the questions remain in the mind of a layman as it is. But we will here try to explain these terms and their meanings in a simple way as possible. 

Artificial Intelligence is simple terms is the study of how to make computers do things which at the moment people do and in a better way.

And both these topics that are Machine Learning and Deep Learning are its subsets such that Deep Learning is a subset of Machine Learning and Machine Learning further is a subset of Artificial Intelligence. See the following figure for greater clarity.

Fig. Artificial Intelligence and its Subsets.

Now Moving Towards What is Machine Learning?

By now you would have got a glimpse of what Learning is and how it is used in computers with the examples that were stated previously.

Machine learning is a technique that gives computers the ability to learn without being programmed exactly for the same.

A classic definition by Tom Mitchell provides a more modern definition: “Let us say that the computer has an experience ‘E’ for a particular task ‘T’ and performance measure ‘P’ such that it learns from experience ‘E’ if its performance in task ‘T’ increases with experience ‘E’.”

Consider this with the help of an example of Playing Checkers:

E: Experience of playing many games.

T: Task of playing Checkers.

P: The probability of winning the game.

In general, we can say that Machine Learning is the process in which algorithms parse the input data, learn from that data, and then apply what they have learned to make decisions.

Now, What is Deep Learning?

Deep learning is a subset of Machine Learning so it is similar to Machine Learning in some ways but has its features too.

Though it is a part of Machine Learning it is based on the concept of Artificial Neural Networks with Representation Learning.

Artificial Neural Networks were inspired by our understanding of the biology of our brains – all the interconnection between the neurons.

It is a machine learning technique that helps the computers to do things like humans i.e. to learn from the examples.

Using deep learning the machine learns to perform classification directly from various data sources such as images, text, or sound. They are also capable of achieving the accuracy that might not be possible for a human to achieve. 

Thus, it tries mimics a human brain for processing the of the data and then obtain the patterns from the data which are then used for decision making.

After knowing the basics of what is Machine Learning and Deep Learning let us, deep-dive, into their differences. But before going towards the differences let’s take an example and understand these concepts individually.

Problem Solving Both my Machine Learning and Deep Learning Approach

Consider the following problem. We have a basket of fruits on our table and we need to classify the fruits like Mangoes, Oranges, Apples, Lemon, etc. Now we have a dataset of the images available for Mangoes, Oranges, Lemon, and Apples. Now let’s solve this problem by both the techniques that are Machine Learning and Deep Learning.

Machine Learning Approach

In the Machine Learning approach, to classify the image data as Mangoes, Oranges, Lemons, and Apples it needs to get structured data stating that this set of images are Mangoes, some of them as Oranges, some as Lemons, and the rest as Apples. Thus, the user needs to tell the algorithm and make it familiar with the data. After which the trained model/algorithm can be tested for the fruit basket to classify the fruits into its specific criteria. If there is any mistake involved in the prediction then the parameter needs to be tuned explicitly by the users and then test it again. This is how the fruits could be identified using Machine Learning Approach.

Deep Learning Approach

In the Deep Learning Approach, it’s a bit different as compared to the Machine Learning Approach. It makes use of Neural Networks and takes a different path to identify the fruits. The most promising advantage of deep learning is that since it makes use of neural networks it does not need to get structured data only. In this case, the data provided is sent through layers of neural network where it is identified for specific features at each layer. At each layer, the images are broken down into smaller and smaller pieces and the information extracted in terms of features is then combined for serving the purpose of classification. Thus, after the final processing, the system is capable of identifying which image is Mango, Orange, Lemon, or Apple.

Thus, we can now move onto the differences finally as you all have an idea of what these two techniques mean.

Thus, summing up onto Machine Learning Vs Deep Learning

  • Human Intervention – While in Traditional Machine Learning models if there is an incorrect result the parameters need to be tuned manually and then adjusted by the humans to achieve the proper results. But this is not in the case of Deep Learning Models, where they have the capability of learning from their own mistakes and do not require any human intervention as the data is passed through multiple layers and layers of neural networks that help them do the same. But a large amount of data being the only constraint.
  • Data Structuring – In terms of data required Machine Learning Models works on structured data in order reach and find a solution, whereas the Deep Learning model is quite compatible as it does not have a constraint of requiring structured data only as it can easily work upon unstructured data due to the use of neural networks which does the work of feature extraction for the same.
  • Amount of Data – Both Machine Learning models and Deep Learning models are used for high volumes of data but when compared relatively to each other Deep Learning Models are capable of handling more amount of data as compared to the Traditional Machine Learning Models.


In this topic, we have discussed quite a lot of basics and some of the advanced concepts of Machine Learning and Deep Learning. But the gist of Machine Learning and Deep Learning relies on the amount and the quality of the data provided to the model. The greater the quality more accurate would be the results. And greater the amount of data more are the cases with which the model deals and becomes aware of the image. For example, in our case of classification of fruits, the more the types of images of Apples, Mangoes, Lemons, and Oranges, the more would be the chances of identifying it correctly in any situation or state. This is what Machine Learning and Deep Learning is which works like a human itself that more the cases you encounter of a particular object, the more the chances become of identifying the object correctly for the next time even if it is not in its standard shape or size.

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