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Harshita Srivastava | 18 Jan, 2023

Machine Learning vs Deep Learning: What's the Difference?

Both machine learning and deep learning are forms of artificial intelligence, however, with some notable differences. While machine learning is a specific application of AI, deep learning is a distinctive form of ML.

In order to make the most out of them, it is important to know how the two subsets of AI differ. Before discussing the various prominent differences between machine learning and deep learning, let’s first get a brief idea about AI, followed by brief descriptions of the two contenders.

AI – This is Where Everything Starts From!

In a less abstract sense, AI or artificial intelligence refers to that branch of study and research that deals with imparting a machine with human-like cognitive ability. Hence, it is important to understand and learn artificial intelligence before delving into ML or DL.

For now, we are still in the premature stages of AI. What this means is that machines that can have reasoning, speech, and understanding levels comparable to human beings are a distant reality. Any AI-powered machine can be listed among one of the three categories:

  • Narrow AI – An AI-powered machine comes under the narrow AI classification if it can perform some specific task better than humans. Currently, we have some artificially intelligent machines that are superior to humans in doing certain tasks.
  • General AI – The next step in AI classification is general AI. It is the stage when an AI-powered machine or computer is able to carry out an intellectual task with the same level of accuracy as performed by a human.
  • Active AI – An artificially intelligent system is one that is superior to human beings in terms of performing several tasks.

Deep learning is a subset of machine learning, while machine learning itself is the subset of artificial intelligence.

Machine Learning – A Specialized Form of AI

Machine learning or ML is a subset of AI. The most fascinating aspect of machine learning is its ability to modify itself when new data is available. This means that ML is dynamic in nature and doesn’t necessitate for human intervention for making changes or modifications.

ML has proven to be a great tool for analyzing and identifying patterns in datasets of varying sizes. The main idea of machine learning is to train a computer or machine to automate tasks that are either exhaustive, impossible, or redundant for an individual to perform.

According to Arthur Samuel, a pioneer in machine learning, ML is a,

“Field of study that gives computers the ability to learn without being explicitly programmed.”

This suggests that machine learning programs aren’t explicitly entered into a computer using if-then and other statements.

In a way, ML programs adjust themselves as a response to the data to which they are exposed to. Although learning ML doesn’t necessitate for having prior AI knowledge, it certainly helps to have a good understanding of AI.

Machine learning makes use of data to feed an algorithm capable to understand the relationship between certain input and output. As soon as the machine completes learning, it can predict either the value or the class of the new data point.

Deep Learning – A Specialized Form of ML

A deep learning model is a software that is capable of mimicking the network of neurons found in the human brain. Typically deep learning refers to mostly deep artificial neural networks and rarely to deep reinforcement learning.

The term ‘deep’ in deep learning signifies the number of layers in a neural network. The more layers a neural network has, the deeper it is said to be.

A deep learning machine makes use of various layers to learn from the data provided. While a shallow network has only one hidden layer, a deep network has multiple layers. A typical deep neural network has three types of layers:

  • The input layer
  • The hidden layer
  • The output layer

As a set of algorithms, deep artificial neural networks has set unprecedented records in terms of accuracy for several important problems. These include image recognition, natural language processing, recommender systems, and sound recognition.

In fact, deep learning is responsible for the creation of DeepMind’s critically-acclaimed AplhaGo algorithm. In 2016, the deep learning algorithm was able to defeat former Go world champion Lee Sedol.

Multiple hidden layers let deep neural networks learn features of available data in a feature hierarchy. Simple features, such as pixels, recombine multiple times in the next layer to yield more complex features, such as lines and shapes.

One of the distinctive features of deep learning is computational intensity. This is why powered-up GPUs are required for training deep learning models.

Machine Learning vs Deep Learning: The Face-Off!

Execution Time

An important distinction between machine learning and deep learning can be drawn in terms of execution time. A typical machine learning algorithm can take anything between less than a minute to a few hours for finishing execution.

Unlike machine learning algorithms, deep learning algorithms require up to several weeks to finish execution. This is due to the fact that a deep artificial neural network requires computing a significantly large number of weights and additional parameters.

Feature Engineering

In feature engineering, domain knowledge is used for creating feature extractors. These reduce the complexity of the data as well as enhance the visibility of patterns. The trade-off for the benefits of feature engineering is that it is time-consuming and requires a high level of expertise.

In the case of deep learning algorithms, there is no requirement for understanding the features or best feature that represents the data. To put simply, DL algorithms don’t require feature engineering. However, the inverse is true for machine learning algorithms.

Hardware Dependencies

While ML algorithms work well on low-end machines, deep learning algorithms necessitate for powerful machines with multiple GPUs. DL algorithms need to compute a significant amount of matrix multiplication, which results in them to demand high-spec systems.


Machine learning comprises of an array of algorithms. While some of them are easy to interpret, such as decision tree and logistic, others are almost impossible to interpret, including SVM and XGBoost. Thus one can say the interpretability of ML varies from easy to impossible.

In the context of deep learning, the interpretability is difficult to impossible. This is the primary reason why implementing deep learning in industrial applications is still a rarity.


Machine learning algorithms perform exceptionally well on a dataset that is small, medium, or somewhere in between. On the contrary, DL algorithms fail to perform well for such datasets. Instead, they perform better for bigger datasets.

The Approach

Machine learning algorithms are used for parsing data, learning from that data, and make informed decisions based on this very learning. On the contrary, deep learning is used for creating an artificial neural network, capable of learning and making intelligent decisions by itself.

Some Notable Applications of ML and DL

  • Computer Vision - Computer vision makes use of ML as well as DL algorithms. Implementations of computer vision include facial recognition and number plate identification.
  • Healthcare - Machine learning and deep learning models have promising applications in healthcare. There has been extensive ongoing research in several facets of the medical field like anomaly detection and cancer identification.
  • Information Retrieval - Another important application of artificially intelligent learning models is information retrieval. This typically refers to search engines with the ability to seek and present appropriate results for image search, text search, and even audio search.
  • Marketing - Automated email marketing, as well as target identification, can benefit from machine learning and deep learning models.

Machine Learning Intro for Python Developers

Machine Learning vs Deep Learning: Head-to-Head Comparison


Machine Learning

Deep Learning


Subset of Artificial Intelligence

Subset of Machine Learning

Execution Time 

From a minute to a few hours

Several weeks

Data Dependency

Can work with small datasets

Works only with huge datasets

Hardware Dependency

Low-end machines

High-end machines

Feature Engineering

Requires a step of feature extraction

Does not require a step of feature extraction

Problem-solving Approach

Splits the problem into sub tasks

Directly provides the end result of a problem

Type of Data 


Structured and unstructured

Suitable for 

Simple problems

Complex problems


Easy to impossible 

Difficult to impossible


High for small datasets

High for large datasets


Both machine learning and deep learning are specialized forms of artificial intelligence undergoing extensive research and offering continuously evolving applications.

Although each of them has several independent applications, the combination of the two facets of AI has demonstrated to reach new heights of success in various research fields.

Hope this article will help you build a clear understanding of the various important differences between deep learning and machine learning models.

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By Harshita Srivastava

Harshita is a graduate from Indian Institute of Technology, Kanpur. She is a technical writer and a blogger. An entrepreneurship and machine learning enthusiast, who loves reading and is a huge fan of Air Crash Investigation!

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