Difference Between Machine Learning and Deep Learning

Nandni Sharma 23 September 2026 Program

What do you understand by machine learning? What are the most common types of Machine Learning? What do you understand by Deep Learning? How does Deep Learning fit inside Machine learning? What is the core difference between Machine Learning and Deep Learning? How to choose between machine learning and deep learning models? Conclusion:

There are 2 terms that you must have heard together in the same sentence, which might make you feel like they are similar in some way or the other. But they are not. One of them is the parent field, whereas the other is a smaller, newer branch inside it.

It is very important to understand the difference between machine learning and deep learning, as it will eventually shape your understanding and project decisions. If you are choosing a course or a project or just want to be informed about it, it is important to know the real difference between machine learning and deep learning.

This article will explain the ideas in simple and readable language. It will avoid any kind of jargon and will make sure to use language that everyone understands.

What do you understand by machine learning?

Machine learning is a process of teaching computer systems to find patterns in data and make decisions without a programmer being required to write code or rules for every single situation.

In previous software, a human was supposed to write exact instructions. In the standard system, you would code saying, “If the temperature is above 30, turn on the fan," but machine learning works differently. In that case, you just have to show the computer hundreds of examples, and it will identify and work out the pattern on its own.

For example, you want to predict house prices. You just have to provide the system with the sales data, such as size, location, number of rooms, and the final price. The system will study this data and learn a pattern that connects these features to price. This is how, next time, it will be able to predict a fair price for a new house.

IT and tech departments have been using machine learning since the 1950s in some form, though it became a lot more common or popular from the 1990s onwards as more data and computers were available.

You can also read about the benefits of machine learning in business in detail here.

What are the most common types of Machine Learning?

Machine learning is generally split into 3 broad groups.

The following are the most common groups under machine learning:

Supervised learning: Under supervised learning, the system learns from labelled examples, like photos that are already marked as “apple” or “pineapple”.
Unsupervised learning: Under unsupervised learning, the computer system tries to identify patterns in data that is not labelled, such as grouping customers by their buying habits.
Reinforcement learning: Under reinforcement learning, the system is supposed to learn through the trial-and-error method. If the computer makes good choices, it gets rewarded.

The most popular machine learning methods include decision trees, random forests, linear regression, and support vector machines.

What do you understand by Deep Learning?

Deep learning is a specific sort of machine learning concept. It basically uses a structure known as a neural network that is built with layers stacked one over another. It is somewhat inspired by how neurones are connected in a brain.

Each layer is supposed to take in some part of the information, change it a little bit, and pass it to the next layer. A network with lots of layers is called “deep”, which is where the name has been derived from.

Deep learning performs the best at tasks where the pattern seems complicated and is buried deep down inside the raw data, such as identifying a face in a picture, understanding spoken words, or translating a sentence from a certain language to another.

Modified deep learning became practical, importantly, after 2012, when researchers showed neural networks could beat older methods at identifying images once enough data and computer power were available.

How does Deep Learning fit inside Machine learning?

After understanding machine learning and deep learning individually, you must be wondering: how do they fit in with each other? Imagine machine learning is a huge circle and deep learning is a smaller circle that sits completely inside it.

Every deep learning system is a machine learning system, but not every machine learning system is a deep learning system. For example, a spam filter that is built with a decision tree is machine learning, but it is not deep learning, as there is no layered neural network doing the work.

What is the core difference between Machine Learning and Deep Learning?

Now, it is time to tackle the biggest fish in the ocean. The following is the real and core difference between Machine Learning and Deep Learning:

Characteristics	Machine Learning	Deep Learning
System and the structure	Machine learning operates on a range of simpler models that are chosen on the basis of the problems.	Deep learning always works on a neural network which is built on various stacked layers.
Feature engineering	In the standard machine learning concept, a person usually has to choose the important details in the data manually. This is known as feature engineering.	In deep learning, these important details are usually identified by the system itself, layer by layer.
Amount of data required	Machine learning models generally work well with a few thousand rows of data.	Deep learning models usually require a lot more data, sometimes millions of examples, to perform well.
Computing power	Traditional machine learning models can run on an ordinary computer.	Deep learning training is heavy work that usually requires a graphics card, which is known as a GPU, or even specialised cloud hardware.
Time taken for training	A machine learning model can usually be trained in minutes. A	A Deep learning model can generally take hours, days, or even longer. That depends on its size and the data involved.
How easy is it to explain the result?	Machine learning models like decision trees are usually easy for an individual to read and explain.	Deep learning models are comparatively harder to explain. This is because the pattern is spread across thousands or even millions of connected figures or values.
Best kind of data	Machine learning models usually seem to do well with clean, table-style data, such as a spreadsheet of numbers.	Deep learning models seem to work well with unorganised or raw datasets, such as images, audio, and free-form text.
Cost invested	The machine learning system usually costs less than a deep learning system.	The deep learning system usually costs more to work well, as the hardware and data require building and running at a huge cost.

How to choose between machine learning and deep learning models?

The most practical question that arises and is asked by most people is how and when to choose between machine learning and deep learning.

The following are the situations according to the models:

You should choose machine learning when:

You have a small or medium-sized dataset.
The data is already clean and organised in a table.
A clear explanation is required, like for loan or medical purposes.
You have a very limited computing budget or time.

You should choose deep learning when:

You are supposed to work with images, audio, or even huge amounts of free text
You have a huge dataset and strong hardware available.
The small improvements in accuracy are required to justify the extra cost and time taken.
The pattern is a little too complicated for a person to describe using simple rules.

Conclusion:

The Difference Between Machine Learning and Deep Learning comes down to structure, data, and cost. Machine learning is the wider field, built from many kinds of models that can learn from data. Deep learning is a specific method inside that field, built from layered neural networks, and it needs more data and more computing power to shine.

Neither one is simply "better". Each fits certain problems well and other problems poorly. A small, neat dataset usually calls for classic machine learning. A large pile of raw images, sound, or text usually calls for deep learning.

Once you see deep learning as a smaller circle sitting inside the bigger circle of machine learning, the rest of the picture falls into place easily, and you will not mix the two terms up again.

You can also read about the difference between machine learning and artificial intelligence in detail here.

Frequently Asked Questions

Yes, deep learning is a branch of machine learning that uses layered neural networks.

The core difference between ML and DL is the structure and data required by both of them individually. Machine learning requires simpler models and comparatively smaller datasets, whereas deep learning requires much larger datasets and stronger hardware.

Machine learning is easier to learn first, as its ideas and maths are simpler compared to deep learning.

Yes; however, it is a little bit hard to justify. Cloud tools have made it quite affordable, but a small business with a small dataset generally receives better value from classic machine learning first.

No, there is no such requirement. Many machine learning jobs focus on easier models and organised data and never even touch a neural network.

I am Nandni Sharma, a Professional content writer who creates informative content about online education, digital learning platforms, and career-focused courses. I aim to help readers find the best opportunities in modern education.
Nandni Sharma