QuickDataScience | Quick & Easy Data Science

Background In today's world of all sorts of Artificial Intelligence coming in, a major element required for the machines to be trained is "Data". A major portion of the data today is generated from social media and mediums like virtual assistants, blogs, news, videos, audio, images, all sorts of papers(research, white) etc. are mostly unstructured. As per current scenarios, there are around  8.5 billion Google searches per day and approximately 2 trillion global searches per year , similarly for Bing we have around 27 billion web searches per month. 37.5 million web searches per hour . But, if we go by industry estimates, less than 25% of the data today is available in structural or tabular data.  So, now the question arises when the data is available in human languages then how we can use them to train machines and get accurate AI machines. The answer for one of the problems, i.e. the textual data is  "Natural Language Processing".  What is Natural Language Pro

  In this series on Natural Language Processing, we are going to cover to entire NLP starting from the basics and gradually moving towards more complex details of the NLP. In the entire journey we are going to focus on both the theoretical as well as practical aspects of the NLP, there are going to be a lot of practical demos and a few sample projects to help you master the NLP.  Introduction to NLP - Getting Started Navigating the NLP Landscape: A Comprehensive Guide to Top Python Libraries Hands-On NLP with NLTK: A Practical Guide from Setup to Practical  Familiarizing with NLTK: Basics of Statistics and Loops for Text Analysis

Artificial Intelligence and ML apps are becoming more pervasive in our everyday lives. They can be used for many different use cases, for instance Virtual personal assistants, Commuting Predictions, recommending products, security, etc. Working in the background, they can provide us with help even when we are not aware they are there. I am sure most of you reading this blog might have received a notification on your mobiles on the best time to leave work based on traffic conditions from various map applications. Or you might have received product recommendations based on your previous purchase pattern. Machine learning applications are rapidly entering our daily lives as technology advances toward providing smartphone mobile-centric solutions. Social Media platforms like Facebook, Linkedin, Instagram, etc are experiencing a rapid progression in recent years. The smartphone's power, productivity, and value must exceed the buyer's expectations to be a successful product. Certainl

  Microsoft Power BI, a name that we all have heard a lot, is also quite famous in the field of Data Science. So, let's look at each aspect of it, including What it is, Why to use it, Who should use it and How to use it? What is Power BI? The first question that comes to mind is What is Power BI? Before we begin, let's see the Official definition of Power BI, Power Business Intelligence.  Microsoft defines it as:-  Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses. Power BI lets you easily connect to your data sources, visualize and discover what's important, and share that with anyone or everyone you want. Wikipedia defines it as:-  Power BI is an interactive data visualization software product developed by Microsoft with a primary

The first question that comes to mind is What is Digital Twin? and How is it related to Data Science and Medicines that we are studying here.  Don't worry we are going to start our post from this point only. What is Digital Twin? As the name suggests, Digital Twin is something related to Twin or a copy of something with a digital presence, or Virtual Presence. So, now the question is What is this "something" of which we create a copy? This "Something" can be anything process, plant, factory, human, machine etc. which is not well defined and might require multiple rounds of mix and match setups to get to the desired/maximum efficiency. These repeated steps not only take a lot of time but also a huge amount of money is also involved in the whole process. Also, there is an uncertainty that the process would succeed or not and how much more time will be required to get the final version.  Let's take a small example to understand the whole concept of the Digital