Educlasses Blogs

Artificial Intelligence vs Machine Learnig vs Deep Learning : Comparision between AI-ML-DL Technologies.

Blogger Name : Mitesh Sharma
Date & Time: 21August2019 & 04:00 PM
artificialintelligence-vs-machinelearning-vs-deeplearning-comparision
Artificial Intelligence, Machine Learning and Deep Learning are the most advance technologies now days. But everyone is confused about these technologies like how these technologies works and related with each other. All major companies have invested lots of money in this sector to achieve this modern technology in their products and services like Google, Amazon and Apple, Microsoft, Uber etc. Some live examples of these smart products, which are running in the world like Apple Siri, Google Assistant, Google Translator, Sophia Robot, Email Spam Filter, Number Plate Detection and many more. Lots of offline and online courses are running to teach this new technology worldwide. People are so courious about this new advance smart technology. But there is a biggest confusion exists till now that what are the differences between Artificial Intelligence, Machine Learning and Deep Learning. So let’s discuss about all of them.
Artificial Intelligence: Artificial Intelligence enables machine to mimic like human brain. Artificial Intelligence is a technology who makes machine to behave like human means to act like human, make pattern like humans and take decisions like human. It makes machine automated and produces decision ability in machine like human. Amazon Alexa and Google Assistant is the proper example of Artificial Intelligence.

Machine Learning: Machine Learning is a technique to achieve Artificial Intelligence. Machine Learning is a subset of Artificial Intelligence. Machine Learning is a technique which uses past data to solve the problem with the help of statically method and trained algorithm model. It learns automatically and will improve with the experience. It trains algorithms based on input labeled or unlabeled data and machine make prediction based on trained algorithms. Email Spam Filter is a proper example of Machine Learning.

Deep Learning: Deep Learning is a subset of Machine Learning. It is an intense extension of Machine Learning. It is a technique to achieve Artificial Intelligence. But it uses Artificial Neural Network to solve the problem and predict the solution. Artificial Neural Network works like Human Biological Neural Network. It solves the problem based on enormous amount of input data with the help of mathematical calculations. It works on complex problem and high end performance machines. Number Plate Detection is the proper example of Deep Learning.
Hence it is cleared that Artificial Intelligence is the outer circle of this technology and both Machine Learning and Deep Learning is the subset of Aritficial Intelligence. Aritficial Intelligence is the last trained output product of this advance technology. This trained output product or machine mimic like human brain. Artificial Intelligence Machines, Devices, Software’s perform task like human. On the other hand Machine Learning and Deep Learning is the only technique or tools to achieve Artificial Intelligence. It uses artificial neural network and different algorithms to make smart decisions based on enormous amount of input data. It makes patterns to solve the problems and achieves artificial intelligence.

So we can say that Artificial Intelligence is a final output of Machine Learning and Deep Learning processes. Artificial Intelligence Machines mimic like human brain. On the other hand Machine Learning and Deep Learning is only a technique or tools to achieve artificial intelligence. But we can say also that Deep Learning is an intense Machine Learning. It is a subset of Machine Learning. So on behalf of that conclusion we are going to find the difference between only Machine Learning and Deep Learning.
Machine Learning

1. Machine Learning is a subset of Artificial Intelligence.
2. It solves small problems.
3. It works on low end machines.
4. It works on different methods and algorithms to solve the problems.
5. It wants small amount of input data (Labeled and Unlabeled).
6. It takes less time to solve the problem.
7. It solves the problems in parts and then combines it to produce output.
8. Example: Google Search Result Refining, Spam Detection etc.
Deep Learning

1. Deep Learning is a subset of Machine Learning.
2. It solves complex problems.
3. It works on high end machines.
4. It works on artificial neural network to solve the problems.
5. It wants enormous amount of input data (Unlabeled).
6. It takes lots of time to solve the problem.
7. It solves the complete problem on end to end system and produce output.
8. Example: Number Plate Detection, Automatic Language Translation etc.
AI-ML-DL are used in following sectors:
Robotics
Healthcare
Ecommerce
Social Media
Entertainment
New Generations
Music Generations
Disaster Detection
AI-ML-DL are used in following operations:
Chatbots
Object Detection
Google Translate
Image Classification
Image Colorization
Image Recognization
Speech Recognization
Number Plate Detection
Live Examples of AI-ML-DL:
Cogito
Pandora
Apple Siri
Amazon Echo
Sophia (Robot)
Google Assistant
IBM Deep Blue Chase
Google Self Driving Car
Happy Learning.
Learn Geoinformatics, Remote Sensing, GIS: Geoinformatics Videos