With the help of this post, we have tried to list down and help you understand the difference between AI, ML, Deep Learning, and Data Science or AI vs ML vs Deep Learning vs Data Science with the help of a few examples.
Post Contents
Introduction
Today, modern technologies like Artificial Intelligence, Machine Learning, Deep Learning, and Data Science have become buzzwords. Everybody talks about them but no one fully understands. They seem very complex to a novice. So we have tried explaining them in a simpler way.
Always, try to learn these with the comparison ai vs ml vs deep learning vs data science as it would be easy to relate and understand.
Artificial Intelligence
Artificial Intelligence (AI) is much different from traditional computer programming, AI is a very broad area and it enables the machine to think like humans and mimic human actions. By using these modules (ML, DL & DS) we derive an AI application.
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals.
Example: Self-driving car
Machine Learning
Machine Learning is a subset of AI and it is a method of data analysis. It provides some statistical tools to explore/enrich the data. Using ML systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Example: Programming of computer chess is an enormous challenge. You can never program every possible move.
With machine learning just show us the patterns, let the machine learn its moves on its own. Neural networks are an extremely robust way for machines to find these patterns.
- Supervised Learning: We will be having small training sets and networks find these patterns and label the data accordingly, with the help of already labeled data and patterns will run against test data and able to predict the future. Real-time example: Correcting a date multiple times, after a certain amount of confidence level it autocorrects the date.
- Unsupervised Learning: We let the neural network to find the patterns in unlabelled data. Unsupervised works well with large datasets. We usually solve clustering kind of issues, which involve segregating data based on similar entities together.
- Hierarchical Clustering
- K-means clustering algorithm
- DBSCAN algorithm
- Reinforcement/Semi-Supervised Learning: Some part of the data will be labeled, and some part of the data will be unlabelled. It learns from past data as well as it will adopt future predictions faster. Here the machine can play games or run algorithms and then look at the result. If a positive result occurs, the machine can learn or reinforce the algorithm.
Deep Learning
Deep Learning is a multilayer neural network architecture (Google’s AI system), Mimics the human brain. Can we make a machine learn like humans? Yes, we can.
- Artificial neural network (ANN) – input in the form of numbers
- Convolutional neural network (CNN) – input in the form of images
- Recurrent neural network (RNN) – input in the form of time series kind of data
- Transfer learning – Extension of ANN, CNN, and RNN.
Data Science
Data Science Overlap with AI techniques and understanding making sense of data. It uses mathematical tools Probabilities, statistics, numerical optimization, Linear algebra, differential calculus.
Data science will work on DL, ML-based on the use case.
AI vs ML vs Deep Learning vs Data Science
We have tried to explain the concepts AI vs ML vs Deep Learning vs Data Science with the help of the below diagram.
Summary
Take away points from this post are:
- AI enables the machine to think without any human intervention.
- ML is a subset of AI and it is a method of data analysis.
- Deep Learning is a subset of ML. The main idea behind DL is to mimic human actions. It uses multilayer neural network architecture.
- Data Science is a technique that applies AI, ML, DL along with mathematical tools such as probabilities, statistics, numerical optimization, linear algebra, and differential calculus.
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