AI vs. Machine Learning vs. Deep Learning: The Difference

Often used interchangeably, the terms AI vs Machine Learning vs Deep Learning lead to confusion among those interested in pursuing a career in the field. If you're one of the students who want to work in this industry, you need to comprehend how these ideas differ from one another.

This blog post aims to help you understand the difference between AI vs ML vs DL. It also aims at the Bachelor of Computer Applications or BCA program as a pathway to a career in this field. 

The BCA course is an undergraduate degree that equips students with the skills required to excel in the world of computer science. BCA students gain hands-on experience in using cutting-edge technology, such as AI, Machine Learning, and Deep Learning. 

Let's dive in and explore the distinctions between these concepts!

What is Artificial Intelligence (AI):

AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the creation of intelligent machines that can perform tasks that typically require human intelligence, such as speech recognition.

(Also Read: What is Artificial Intelligence)

What is Machine Learning (ML):

Machine Learning involves the use of algorithms to enable machines to learn from data and improve their performance over time without being explicitly programmed. It is an application of AI that allows systems to automatically improve their performance by learning from experience.

What is Deep Learning (DL):

Deep Learning involves using neural networks to enable machines to learn from data and make decisions like humans. It is inspired by the structure and function of the human brain and consists of multiple layers of artificial neural networks that can learn and make decisions on their own.

Overall, AI is the broadest concept that refers to the creation of machines that can perform human-like tasks, ML is a subset of AI that uses algorithms to enable machines to learn from data, and DL is a subset of ML that involves the use of neural networks to enable machines to learn and make decisions on their own.

Examples of ML vs AI vs DL

Machine Learning (ML) Examples:

• Spam detection filters in email services, where ML algorithms are used to learn patterns and characteristics of spam messages.
• Recommendation systems in e-commerce, where ML algorithms are used to recommend products to customers based on their past purchases and browsing behaviour.
• Credit scoring systems in finance, where ML algorithms are used to analyse data and predict credit risk for loan applicants.

Artificial Intelligence (AI) Examples:

• Personal assistants like Apple’s Siri, Amazon’s Alexa, and Google Assistant use AI to understand natural language and perform tasks for users.
• Self-driving cars use AI to recognise objects, navigate roads, and make decisions.
• Medical diagnosis and treatment planning systems use AI to analyse patient data and provide recommendations to healthcare professionals.

(Read Also: Career in AI After BCA)

Deep Learning (DL) Examples:

• Image and speech recognition, where DL algorithms are used to identify objects in images and transcribe speech to text.
• Natural language processing, where DL algorithms are used to understand and generate human-like language.
• Autonomous systems, such as drones or robots, use DL to learn how to navigate and perform tasks in complex environments.

In summary, ML is used for tasks like prediction and classification, AI is used for tasks that require human-like intelligence, and DL is used for tasks that involve complex data like images, speech, and text.

Difference Between Ai and ML, and DL

The terms Machine Learning vs Deep Learning vs Ai have become ubiquitous with the rapidly advancing technology. These three concepts have gained a lot of attention due to their potential to revolutionise different industries, including healthcare, finance, and education. 

Here is a table summarising the differences between ML vs DL vs Ai (Machine Learning vs Deep Learning vs Artificial Intelligence).

Particulars	Al	ML	DL
Definition	The simulation of human intelligence in machines.	The use of algorithms to enable machines to learn from data and improve their performance.	The use of neural networks to enable machines to learn and make decisions on their own.
Data Dependency	Can function without data.	Requires data to learn and improve performance.	Relies heavily on data and large datasets.
Complexity	Can be simple or complex, depending on the task.	Less complex than DL and uses simpler algorithms.	The most complex of the three. Involves the use of multiple layers of artificial neural networks.
Algorithms	A broad range of algorithms.	A specific set of algorithms, such as decision trees and regression.	Neural networks with many layers, such as convolutional neural networks and recurrent neural networks.
Input	Predefined rules, data, and/or human inputs.	Labelled and/or unlabeled data.	Large and complex data, such as images and natural language.
Output	Pre-programmed rules and/or human inputs.	Predictions or classifications based on data inputs.	Predictions or classifications based on complex data inputs.
Examples	Personal assistants, self-driving cars, medical diagnosis and treatment planning.	Spam detection filters, recommendation systems, and credit scoring systems.	Image and speech recognition, natural language processing, and autonomous systems.

Final Words

So, if you want to learn more about the difference between ML, DL and Ai while also gaining a comprehensive understanding of computer science, then pursuing a BCA program could be the perfect choice for you. Moreover, we advise picking a college that provides the benefits of Sunstone - a leading higher education services provider.  

More than 50 colleges and universities in 35 Indian cities are powered by Sunstone which help you get in-demand advanced certifications, placement support, industry-aligned training modules and guaranteed interviews with the help of over 1200+ recruiters from across the country.

FAQ - AI vs. Machine Learning vs. Deep Learning 

Which is better, ML or DL?

It depends on the task. ML is better for simpler tasks and smaller datasets, while DL is better for more complex tasks and larger datasets. DL requires more data and processing power, so it may not always be the best choice.

What should I learn first, DL, AI, or ML?

It depends on your goals and background. If you are new to the field, it is recommended to start with ML and then move to DL. AI encompasses a wider range of concepts and may require a deeper understanding of ML and DL.

Which is tough, AI or ML?

Both AI and ML can be challenging, but it ultimately depends on the task and the level of complexity. DL is considered the most challenging due to its complexity and reliance on large amounts of data and processing power. However, with the right resources and approach, anyone can learn and master these concepts.

(Note to Reviewer: To avoid the usage of whole sentences in a table, which defies the point of a table, we have kept the content crisp for better readability. )