Artificial Intelligence Vs Machine Learning Vs Deep Learning
Today we are living in a world that is truly dependent on the machines and technologies.
Most of our essential tasks are being managed or carried out by smartphones and other smart devices.
Ever wonder how the system works? And what is the meaning the today’s trending keywords like artificial intelligence , machine learning, and deep learning? How are they interrelated with each other? And how do they work together to make our lives easy? All of these questions will be answered in this article.
Whether you are a student of data science or just a curious person who is interested in this domain,
this article will be beneficial for you.
Artificial Intelligence (AI)
According to Wikipedia –
Artificial intelligence is the capability of computational systems to perform tasks typically associated with human intelligence , such as learning, reasoning, problem-solving, perception, and decision-making.
In simple terms,
“AI is the ability of the machine to exceed or match the intelligence of a human.”
This can include :
– Learning from experience
– Understanding language
– Recognizing images
– Making decisions
On the ground level , AI can be seen as a program that instructs the machine to function in a certain way to the situation . So we can a bunch of if-else statements.
Example : Self -driving cars , smart chatbots , game-playing bots , etc.
Machine Learning (ML)
Machine learning is a branch of AI that uses algorithms to analyze data, learn from it, and make predictions or decisions without explicit programming.
You’ve likely encountered Gmail’s feature that automatically classifies emails as spam or not spam by examining their content. Similarly, when shopping on e-commerce websites like Flipkart, Amazon, or Myntra, after searching for an item (like T-shirts), the site recommends related products (like jeans or shirts). Streaming services such as Netflix or Hotstar suggest content based on your previous preferences.
All these features are powered by machine learning.
How do these impressive capabilities work? Machine learning requires large volumes of data to train using popular ML models. The professional who trains these models is called a data scientist.
Machine learning can be broadly classified into three segments :
i) Supervised Learning
ii) Unsupervised Learning
iii) Reinforcement Learning
Supervised learning :
This method trains the model with labeled data such as email classifiers or voice recognition.
Algorithm inlcludes Linear Regression , Logistic regression ,SVM ,etc.
Unsupervised Learning :
This method trains the model on the unlabelled data. Unlabelled data is used to identify hidden patterns , such as cluster (group) analysis and customer segmentation.
Algorithm includes K-means clustering , K- Nearest Neighbour , PCA
Reinforcement Learning :
It is an area of ML concerned with how intelligent agents take actions in an environment to maximize rewards.
Let’s understand in depth :
Here 4 terms are used –
Environment – Where the action is performed.
Agent – The Performer
Action – Action that is being performed
Reward – Getting a reward after each
Example : In a chess game , chessboard is the environment and agent is the computer , action is his moves in the game, and the reward is winning that game.
Games like chess use reinforcement learning.
ML follows a structural path in order to work properly .
Data Aquistion → cleaning the data → ML model training → Evaluation → Monitor , Update → Hypertuning
Together all these steps work to make the model predict accurately and efficiently.
Deep Learning :
Deep Learning is a subset of ML that mimics human brains using ANN ( Artificial Neural Networks).
Unlike ML, DL can automatically extract features from raw data , making it highly effective for unstructured data like images , audio and text.
Deep learning models automatically discovers complex patterns using multiple layers of computation – hence the term “deep” learning.
Deep learning is the best for image recognition, speech recognition ,NLP , self driving cars ,etc.
It requires large datasets and highly computationals powers (GPU).
Key components of Deep Learning :-
– Neural Networks – Layer of interconnected nodes(neurons)
– Deep Neural Networks (DNN) – Multiple hidden layers for complex learning.
– Convolutional Neural Network (CNN) – Used in image recognition.
– Recurrent Neural Network (RNN) – Used in sequential data (speech, text)..
Conclusion:
AI ,ML ,DL are interconnected but serve different purposes : –
– AI is the goal to make smart machines.
– ML is the method to teach machine using data.
– DL is the tool for solving complex problems with big data.
Each layer is powerful and together they shape the future of tech . Understanding the difference helps in choosing the right approach for solving real world problems.