Everything you should know about Machine Learning Vs Deep Learning: How AI subsets differ in handling these?

Everything you should know about Machine Learning Vs Deep Learning: How AI subsets differ in handling these?

When it comes to speaking about today’s technology, Artificial Intelligence, Machine Learning, Deep Learning is the most popular topics being discussed. People usually get confused between all these three. But the thing, there are certain differences one should be aware of.

As the world is moving towards automation for every business, all these technologies together are responsible for making this happen. So, what are these technologies? How are they differentiated? This blog discusses all the answers to these queries.

ML Vs DL: What is Artificial Intelligence here to do?

Artificial Intelligence is the process of developing intelligent machines. This is completely different from the rest two of them. They come under the subsets of AI. AI is here to deal with the process of imparting data, information, and human intelligence to machines.

Additionally, the motto of AI is to come up with a self-reliant machine which can perform the human processes effectively without any errors. Since there are huge possibilities of errors to happen with humans, this AI can be an effective way to sort them out.

These systems are loaded with natural intelligence system which can solve complex problems. Let’s consider a real-time example of AI. Amazon Echo is a smart speaker that makes use of Alexa, which is a virtual AI technology built by Amazon.

This Alexa tool has the capability of voice interaction, playing music, games, audios, videos, as well as providing real-time news and information happening across the world in minutes. For instance, if a person asks for the current temperature in a particular location, Alexa automatically converts it into machine-readable language, processes the answer, and comes with the output.

Most of the Software Development Companies have realized the importance of AI and have event started to work on it.


  •  Machine Language Translation such as Google Translate.
  • Self-driving vehicles such as Google Waymo.
  • AI Robots such as Sophia.
  • Speech Recognition applications like Siri, Ok Google, Cortona.

Artificial Intelligence is critical in these applications since they are responsible for gathering the user data, analyzing them, processing the results, and offering the best possible outcomes as expected by the users. Without doubts, AI is here to stay since it has the ability to handle any kind of irrespective of the size. Also, they are more compelling in accessing, analyzing, and processing data within a short span of time.

In general, there are three types of AI for now:

1. Narrow AI

Narrow AI is the current phase. This AI can solve specific tasks but better than humans. Hence, it is treated as a Narrow AI.

2. General AI

 This is the second stage of AI where it can perform intellectual tasks in an effective way with the same accuracy as humans.

 3. Active AI

 As the name suggests, this one is here to deal with complex problems that are better than humans.

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Machine Learning: How it differs?

When it comes to Machine Learning, it is totally different from the former one. Machine Learning is a subset of Artificial Intelligence which is the best tool to analyze and process huge patterns of data. It can automate all the tasks which are more effective in performing human tasks.

Let’s know how ML works

  •  Gathering the data.
  • Data Pre-Processing.
  • Choosing the model.
  • Training the model.
  • Test the model.
  • Tuning the model.
  • Prediction.

If in case you have built a program which recognizes objects. It would definitely need a classifier that identifies the class it belongs to. For instance, you have a program where a classifier has to identify the following things:

  • Bicycle
  • Train
  • Flight
  • Bike
  • Dogs

The classifier here is responsible for recognizing the objects and classifying them. In order to construct a classifier, you should have some data as input and assign corresponding labels to it. The algorithm will access those data, design a pattern for it, and then classifies them into the corresponding classes. This is termed as “Supervised Learning”. In this learning, the training data which is loaded to the algorithm comes with a label.

Training an algorithm will come up with the following steps:

  • Collecting the data.
  • Training the classifier.
  • Making Predictions.

Initially, the classifier collects the data and the chosen data is called “Feature”.

Secondly, the corresponding algorithm is chosen to train the model. Once the training is done, the model will start with predictions.


  • Sales Forecasting
  • Fraudulent Analysis
  • Product Recommendations
  • Stock Price Predictions

While the case of “Unsupervised Learning”, comes with algorithms which employ unlabeled data to figure out the patterns from the collected data. The System can also identify hidden features in the data and once it is found readable, it generates patterns.

Yet another one is “Reinforcement Learning”, there comes an agent who should be trained to perform tasks within the environment. The agent receives observations and a reward from the environment and sends the completed actions to the environment.

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Deep Learning: How it differs?

 Finally, Deep Learning is a subset of Machine Learning, which deals with algorithms that are inspired by the structure and function of a human brain. It deals with both structured and unstructured data irrespective of how massive they are. The core concept lies in artificial neural networks, which help machines to make decisions. The primary difference between Deep Learning and Machine Learning lies in the way the data is presented to the machine.

 Let’s see how DL works:

  • An Input Layer which accepts the data.
  • A Hidden layer that figures out any hidden features from the data.
  • An output layer that offers the exact output.

These deep neural networks are classified into three:

  • Convolutional Neural Networks.
  • Recurrent Neural Networks.
  • Generative Adversarial Networks.
  • Deep Belief Networks.

Deep Learning also comes with an automatic data extraction feature. Let’s know how it works:

A dataset contains a dozen to hundreds of features. The system has the responsibility of learning the relevance of the features. Anyhow, not all the features are captured by the algorithm. This is the most crucial part of Deep Learning. Either they can make use of Machine Learning for feature extraction. This extraction combines with the existing features in order to create more new features. This is done with many algorithms. Deep Learning is here to solve the complex issues prevailing with neural networks.


 Without any doubt, AI and its subsets will be responsible for discovering a new world. To make the business operations hassle-free, these advanced technologies will be highly beneficial. If your business hasn’t implemented it, you can do this right away.

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