AI vs ML vs Deep Learning vs Data Science

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.

Wikipedia Definition:

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.