Do you want to know the difference between Machine Learning vs AI vs Data Science vs Deep Learning? If yes, then give your few minutes here. After the end of the blog, you have a clear idea about Machine Learning vs AI vs Data Science vs Deep Learning, and how they are correlated?
Hello, & Welcome!
As we all know that Machine Learning, AI, Data Science & Deep learning are very popular terms in the field of Computer Science. Most of the techie people want to learn these technologies. Nowadays, every electronic gadget which is coming in the market is full of AI functionalities. Everywhere these technologies are using from business to education.
Therefore without wasting your time, I would like to discuss the differences between Machine Learning vs AI vs Data Science vs Deep Learning.
Machine Learning vs AI vs Data Science vs Deep Learning-
Firstly, I would like to start with-
“Machine Learning“… after hearing this word, you suddenly get a thought that “Machine Learning” is the process where a machine is learning something. Of course, you are right!
Machine Learning is the application that allows machines to learn and improves their performance by itself. In machine learning, some set of instructions are given in the form of training the model. On the basis of training data, the machine learning model learns and predicts the outcome.
For example, we build a model to differentiate between cat and dog. So firstly, we provide some images of cats and dogs to our model in the training phase. The model learns from training data. Model differentiate between dog and cat based on different parameters like face shape, length of ears, eye color, etc. After the model has been trained, we randomly give some images to model to predict whether its cat or dog? The model predicts the result according to its learning.
You can consider a machine learning model as a newborn child, who learns in the same way as machine learning learns. The newborn child learns from the instruction given by his parents and by his own experiences. He learns walking by falling again and again and corrects himself and try again walking. Machine Learning does the same job, the model learns and improves performance by correcting the mistakes.
Process of Machine Learning–
Process of Machine Learning includes mainly 5 steps-
- Data Collection- The first step in machine learning is data collection. For training the model, this data is used.
- Data Cleaning- The data we collect is full of noise and not in a proper format. This step performs all the tasks related to data cleaning.
- Training- Once the data cleaning complete, this clean data is used for training the model. Model learn from this training data.
- Testing- After the training phase, the model learns, now it’s time to test its performance. In testing, randomly some data is given, and the model has to predict it on the basis of its knowledge.
- Tuning- If the model is not accurate as needed so in this step, performs tuning to improve the model performance.
Some Best Machine Learning Courses-
- Machine Learning (Coursera)
- Deep Learning Specialization (deeplearning.ai)
- Machine Learning with Python (Coursera)
- Advanced Machine Learning Specialization
- Get started with Machine Learning (Codecademy)
- Learn the Basics of Machine Learning (Codecademy)
- Mathematics for Machine Learning Specialization (Coursera)
- Machine Learning A-Z™: Hands-On Python & R In Data Science -Udemy
- Python for Data Science and Machine Learning Bootcamp- Udemy
- Machine Learning Engineer Masters Program (Edureka)
- AI & Deep Learning with TensorFlow (Edureka)
- Intro to Machine Learning with TensorFlow (Udacity)
- Become a Machine Learning Engineer (Udacity)
- Deep Learning (Udacity)
Artificial Intelligence (AI)-
As the name sounds, ” Artificial Intelligence” means Intelligence which doesn’t belong to humans. It belongs to machines.
In other words, the objective of AI is to make machines as intelligent as humans. Machines react like a human being. A machine can make an intelligent decision as humans do.
And surprisingly, AI accomplished the objective. Nowadays you can see everywhere AI is present. The robots are full of ai. Self-driven cars are coming into the market.
Machine learning is the subpart of AI.
Data Science is somewhere related to all of us because we are generating a huge amount of data daily. This data is anything, it may be one facebook likes, one-click on any website, one image upload, etc.
According to one report, By 2025, it’s estimated that 463 exabytes of data will be created each day globally – that’s the equivalent of 212,765,957 DVDs per day!
As the data growth is too high, data science comes into the scene, data is not in the proper format, but it contains very valuable information for the business. If we find certain patterns from data, it plays an important role in the business industry. This data can turn into revenue if we find patterns.
For example, if someone buys milk and bread together so put milk and bread together in a supermarket to increase the sale of both items. By putting together both items, a person who comes to buy only milk might purchase bread too. So using such tactics there is an increase in market growth.
Some best Data Science Courses-
- IBM Data Science Professional Certificate
- Data Science Specialization
- Applied Data Science with Python Specialization
- Data Engineering, Big Data, and Machine Learning on GCP Specialization
Deep learning is an advanced form of Machine Learning. If you have a small dataset and you want to make a model, then machine learning works perfectly. But if you have a large dataset and many features present in your dataset then machine learning algorithms fail to perform.
Here, deep learning is used. Deep learning works perfectly fine with large datasets and with lots of features. Deep learning works on artificial neural networks, which is the same as the human brain, where neurons are connected. There are three layers, input layer, hidden layer, and output layer.
Mostly all industries are using Deep Learning. It is very powerful as compared to Machine learning, that’s why it requires powerful hardware (GPU) to run. Deep learning is complex as compared to machine learning.
Application of Deep learning is speech recognition, image recognition, natural language processing, medical image analysis.
I hope now you have to understand the difference between all these terms. If you have any doubt, feel free to ask me in the comment section.
Machine Learning allows machines to learn in the same manner as a human learns. Machine Learning predicts the outcome with the help of data. Whereas Data science is the broad term.
Artificial Intelligence is the broad term, and Deep Learning is the subpart of AI. The goal of AI is to mimic humans, machine learning allows machines to learn, but Machine Learning work only on a small dataset. To process a huge amount of data, deep learning is used.
If you want to make a model in machine learning, then knowledge of programming language is mandatory.
Are you ML Beginner and confused, from where to start ML, then read my BLOG – How do I learn Machine Learning?
If you are looking for Machine Learning Algorithms, then read my Blog – Top 5 Machine Learning Algorithm.
If you are wondering about Machine Learning, read this Blog- What is Machine Learning?
Enjoy Machine Learning
All the Best!
Though of the Day…
‘ Leadership and learning are indispensable to each other. ‘– John F. Kennedy