Machine Learning and AI: From Basics to Mastery

Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science

Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science

These have become the new buzz words all around the media and people often confuse them with what each of the words actually means. If you too are one of them then this article will resolve it for you. Though the definitions of these terms are not quite concrete in the tech industry and are quite often used interchangeably as these fields are fast evolving.

Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science. Let us try to understand each one of them

1. Artificial Intelligence

The concept of AI came into existence in 1950 and the term was coined in 1955 by John McCarthy.

AI is something that enables machines to think like humans and emphasizes the creation of intelligent machines that mimic human behavior.

There have been various stages of AI initially from quite an old school rule-based AI to today's Human brain mimicking Deep Learning.

2. Machine Learning

The term Machine Learning was coined by Arthur Samuel in the late 1960s. It is a subset of artificial intelligence where computers learn from data.

Machine Learning is the ability to automatically learn and improve from experience without being explicitly programmed using data. It uses statistical tools and methods to enable machines to improve with experience and make data-driven decisions to carry out any kind of task. It's like teaching machines using examples.

3. Deep Learning

The term Deep Learning came about in the late 1980s but it really took off in the year 2012 when Deep learning started to gain all its popularity and major breakthroughs happened from then on.

Deep Learning is a special area in Machine Learning which is based on Neural Networks a kind of model architecture that is inspired by the functionality of our brain cells called neurons. Neural Networks closely mimic the working of the human brain and learns complex function mapping without depending on any specific type of ML algorithm.

The key fuel to Deep Learning model is a huge amount of data and a high amount of computational resources. More the compute and more the data the better it gets in learning the data patterns from a lot of examples but all that happens at the cost of ML Model abstractions and hence it is a black box

Deep Learning is responsible for most of the advances that we hear about in AI in the applications of Siri, Self Driving Cars, Speech Recognition, etc.

4. Data Science

Data Science is a quite old concept but the term was popularized in 2008 by DJ Patil and Jeff as understanding and making sense of data.

Data science is an interdisciplinary field of studying data with scientific methods, processes, algorithms, and systems to extract knowledge and insights to reach actionable conclusions. Here the can be in any form structured, unstructured or semi-structured. At the core, Data Science uncovers and surfaces hidden findings from data and explores data at a granular level to mine and understand complex behavior, trends, and inferences. And enables to make smarter business decisions and impact at scale.

Data Science is a broader concept which uses AI and ML for its applications to gain insights to unleash business value and extend Data Science to its fullest potential.

So in a nutshell.

AI: Mimicking humans

ML: Learning with experience using data

DL: Self learn with more data using Neural Networks

DS: Understanding and finding hidden insights in data to reach actionable conclusions

We hope you were able to understand what the buzz words actually mean through this blog post. Now you know the answers whenever someone asks you the difference between Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science.

Here at Board Infinity, we cover all the above topics in quite a depth with real-world case studies in our Data Science Learning Path.