Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence makes computers smarter, Machine Learning helps them learn from data, and Deep Learning improves learning using neural networks. However, each works in a different way. To understand the difference between them, we need to look at how they work, their real-world applications, and the unique role each plays in modern technology.

In this blog, we will learn what is the difference between artificial intelligence, machine learning, and deep learning in more detail to get a better understanding.

Table of Contents

• • What is Artificial Intelligence (AI)?
  • What is Machine Learning (ML)?
  • What is Deep Learning (DL)?
  • Machine Learning vs Deep Learning vs Artificial Intelligence (ML vs DL vs AI)
  • ML vs DL vs AI: Examples
  • Examples of Deep Learning in Real-world Applications
  • AI vs. ML vs. DL works: Is There a Difference?
  • Conclusion

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a technology that enables computers and machines to think, learn, and make decisions like humans. It enables them to understand the data, identify patterns, and find solutions without step-by-step guidance for each task or program.

The below diagram gives more clarity on Artificial Intelligence vs Machine Learning vs Deep Learning:

1. How does Artificial Intelligence(AI) work?

AI works by using algorithms, models, and large amounts of data to act like human intelligence. It can perform tasks such as:

• • Understanding speech (like Siri and Alexa)
  • Recognizing images (like Face ID on your phone)
  • Making recommendations (like Netflix and YouTube)
  • Playing games (like chess-playing AI)

What is Machine Learning (ML)?

Machine learning is a subset of artificial intelligence, that allows machines or computers to learn from data and improve their performance over time by continuously learning. This makes it useful for tasks like recommending movies, detecting spam emails, and recognizing speech or images.

1. How Machine Learning Works?

Machine learning works by training a computer to learn from data and make predictions or decisions. The process involves:

• • Data Collection: The first step is to collect the data, the system retrieves the data from various sources such as images, text, or numbers.
  • Data Processing: In the second step, the collected data is processed and analyzed to reduce errors and give accurate results.
  • Model Training: The third step is to train the model, Here the machine learns form the data using algorithms like decision trees or neural networks to find patterns.
  • Model Testing & Evaluation: After the training phase, the trained model is now tested with new data to check its accuracy and performance.
  • Predictions & Improvements: After the testing phase, machine learning models make predictions based on the data, also it improves the datasets by continuously learning from the data.

What is Deep Learning (DL)?

Deep Learning (DL) is a specialized branch of Machine Learning that uses artificial neural networks to work like a human brain. It enables computers to learn from large amounts of data and find patterns automatically.

Deep learning is used in many advanced AI applications, such as facial recognition, self-driving cars, and voice assistants. It is particularly useful for tasks like image and speech recognition, language translation, and medical diagnosis. Because deep learning models can analyze vast amounts of data, they continue to improve their accuracy over time.

1. How Deep Learning Works?

Deep Learning works by using artificial neural networks, which are designed to work like the human brain. These networks have multiple layers of interconnected nodes (neurons) that process information step by step.

First, the system takes in raw data (like images, text, or speech) and passes it through different layers of neurons. Each layer extracts important features, learns the pattern, and improves accuracy over time. With more data and training, deep learning models get better at making decisions and understanding language.

Machine Learning vs Deep Learning vs Artificial Intelligence (ML vs DL vs AI)

Here are the following differences between machine learning, deep learning, and artificial intelligence:

ML vs DL vs AI: Examples

Here are the following examples of machine learning, deep learning, and artificial intelligence:

1. Examples of AI in Real-World Applications

Here are some of the examples of AI in real-life applications:

• • Virtual Assistants & Chatbots: We can use AI-generated assistants like Siri, Alexa, and Google Assistant to set reminders, answer queries, etc.
  • Healthcare & Medicine: In healthcare, AI helps doctors to detect diseases, predict health problems, and suggest treatments.
  • E-commerce & Recommendations: AI suggests products based on what you like and what type of products you have bought in the past\
  • Autonomous Vehicles (Self-Driving Cars): AI helps self-driving cars to see the road, avoid obstacles, and drive safely.
  • Finance & Banking: AI detects fraud, helps with online banking, and gives financial advice through chatbots.

2. Examples of Machine Learning in Real-world Applications

Here are the following examples of machine learning in real-world applications:

• Spam Detection: Machine learning is used in email services to filter out spam messages based on specific patterns and keywords.
• Product Recommendations: Machine learning also recommends you products based on your browsing and purchase history.
• Voice Assistants: Siri, Alexa, and Google Assistant learn from user interactions to provide better responses.
• Medical Diagnosis: AI-powered systems analyze medical data to help doctors detect diseases early.

3. Examples of Deep Learning in Real-world Applications

Here are the following examples of deep learning in real-world applications:

• Image and Facial Recognition: It is used in smartphones, social media platforms, and security systems to identify and authenticate individuals.
• Autonomous Vehicles: Self-driving cars use deep learning to detect objects, recognize traffic signs, and make driving decisions.
• Healthcare & Medical Diagnosis: AI-powered models help detect diseases like cancer by analyzing medical images such as X-rays and MRIs.
• Natural Language Processing (NLP): Voice assistants like Siri, Alexa, and Google Assistant use deep learning for speech recognition.

AI vs. ML vs. DL works: Is There a Difference?

Here are the following differences between the work of artificial intelligence, machine learning, and deep learning:

1. What Does an AI Engineer Do?

An AI engineer creates computer systems that can learn and make decisions like humans. You work with data, build AI models, and train them to improve over time. Their work responsibilities include the following steps:

• Builds Smart Systems: They create programs or machines that can learn from data and improve over time.
  • Examples: Chatbots, recommendation systems (like Netflix or Amazon suggestions), or self-driving cars.
• Works with Data: They collect and prepare data (like images, text, or numbers) to “teach” the AI system. They also clean and organize data so the AI can learn effectively.
• Trains AI Models: They use algorithms (step-by-step instructions) to train the AI to recognize patterns or make decisions.
  • Example: Teaching an AI to recognize cats in photos by showing it thousands of cat pictures.
• Writes Code: They use programming languages like Python to build and test AI systems. They work with AI frameworks like TensorFlow, PyTorch, or OpenAI tools.

2. What Does a Machine Learning Engineer Do?

A Machine Learning Engineer builds systems that allow computers to learn from data and make predictions. Their work involves handling large datasets, selecting the right techniques, and optimizing models for real-world applications. Machine learning engineers collaborate with data scientists and software developers to create AI-powered solutions like recommendation systems, chatbots, and fraud detection tools.

3. What Does a Deep Learning Engineer Do?

Deep Learning Engineers are responsible for creating and improving AI models that act like human brains using neural networks. They work with large amounts of data to train machine learning models that can recognize images, process speech, and understand language. Their job role involves designing deep learning architectures, optimizing algorithms, and using frameworks like TensorFlow or PyTorch. 

Conclusion

So far in this blog, we have learned the difference between artificial intelligence, machine learning, and deep learning. Artificial intelligence is the technology that helps the computer to think like humans, machine learning is the subset of artificial intelligence that allows computers to learn from data and make decisions without manual coding, and Deep Learning (DL) is a branch of Machine Learning (ML) that uses artificial neural networks to process and learn from large amounts of data.

If you want to learn more about these technologies, you may refer to our Machine Learning, Artificial Intelligence, and Deep Learning Courses.

Frequently Asked Questions (FAQs) 

Q1. Is ChatGPT ML or DL?

ChatGPT is a Deep Learning (DL) model because it uses advanced neural networks to understand and generate text. Deep Learning is a part of Machine Learning (ML).

Q2. Is NLP part of ML or DL?

Natural Language Processing (NLP) is a type of Machine Learning (ML), but modern NLP often uses Deep Learning (DL) to improve accuracy and understanding.

Q3. Can I learn NLP without ML?

You can learn some basic NLP concepts without Machine Learning (ML), but for advanced tasks like chatbots and translations, ML is needed.

Q4. Is DL better than ML?

Deep Learning (DL) is better for complex tasks like recognizing images and speech, but it needs a lot of data and computing power compared to Machine Learning (ML).