In the coming decades, quantum computation will play an essential role in technology, science and business advancement. With Google claiming “Quantum supremacy” and exponential interest in this subject, we aren’t far from seeing many intractable issues challenging to tackle with a classical computer solved by Quantum processing. Furthermore, there is a clear indication that machine learning and quantum computation will play complementary roles in strengthening each other’s areas.

But the main question is why now one should dive into a complex topic like Quantum Machine Learning that looks complex and difficult to learn.
Fortunately, Quantum Computation and then Quantum Machine learning…

In-Depth Analysis

Last Updated : 12th February 2020

Most of the Machine learning algorithms can not handle categorical variables unless we convert them to numerical values. Many algorithm’s performances vary based on how Categorical variables are encoded.

Categorical variables can be divided into two categories: Nominal (No particular order) and Ordinal (some ordered).

Scientists, engineers, and AI experts have drawn inspiration from their environment to create various Artificial Intelligence and Machine Learning algorithms. Deep learning has ushered in an AI revolution in recent years, beginning with inspiration from the human brain, especially neurons. In this article, I’ll highlight a few well-known algorithms or disciplines of Artificial Intelligence and Machine Learning that have been strongly influenced by nature. Fortunately, nature does not have a patent; otherwise, we would have to ask bees, fish, or even ants for permission.

Flower Pollination Algorithm (FPA)

Reinforcement learning (RL) is the hottest field of Artificial Intelligence and Machine Learning with many breathtaking breakthroughs in the last couple of years. This article is an attempt to give a concise view of the entire RL spectrum without going too much into math and formula while not losing sight of trees in the dense & complex forest.

Now first, let’s understand what is Reinforcement Learning or RL?

RL means taking optimal action with long term results or cumulative rewards in mind.

In day to day, data analysis, we face many unique situations for which Pandas built-in API has many hidden treasures that can resolve most of our problems through concise, elegant code. In this article, I will share, few simple yet compelling tricks that can make programming a joyful experience. When I used some of them for the first time, I had a “wow” effect.

We will use below DataFrame to explain all the concepts used in this article.

Python is a language of expressiveness in concise code, which is very elegant and easy to comprehend. List Comprehension provides a concise way to create lists. List Comprehension is the proper “pythonic” way of building an accessible, concise, and fast way to build lists. It allows one to perform complex operations on lists using a single line.

What are List Comprehensions?

List comprehensions provide us with a simple way to create a list based on some sequence or another list that we can loop over. In python terminology, anything that we can loop over is called iterable. At its most basic level, list comprehension…

Ever since corona has hit the earth with its deadly menace, the use of AI and Machine learning helped countries to identify & contain the spread of the virus, distribute limited resources, assist in deciding the next course of action.

AI technologies, in areas including natural language processing, speech recognition, computer vision, data analytics, machine learning, and deep learning, are now being deployed as powerful weapons against COVID-19. This article is a collection of stories narrating how successful application of technology, particularly Artificial Intelligence, Machine Learning and Data Science taking the battle head-on against deadly coronavirus to save our habitat.

Understanding Virus Inside Out :

IN DEPTH ANALYSIS

Machine learning is like making a mixed fruit juice. If we want to get the best-mixed juice, we need to mix all fruit not by their size but based on their right proportion. We just need to remember apple and strawberry are not the same unless we make them similar in some context to compare their attribute. Similarly, in many machine learning algorithms, to bring all features in the same standing, we need to do scaling so that one significant number doesn’t impact the model just because of their large magnitude.

Feature scaling in machine learning is one of the…