What is Natural Language Processing? A Beginners Guide

Posted by Srot Sinha on Apr 9, 2020 8:30:00 AM

In this blog we will answer the most common questions asked by beginners in NLP.

Question: What is Natural Language Processing?

Answer: Natural Language Processing (NLP) is a programming technique by which computers can understand the language spoken or transcribed, based on the intent. It sounds very trivial, but it is not! Understanding a language is very difficult, as people can have conversations in many different ways, and based on tone and connotation, can change the meaning of the conversation.

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Topics: Artificial Intelligence, Natural Language Processing, NLP

Data Analysis Part 5: Data Classification, Clustering, and Regression

Posted by Alexis Vander Wilt on Apr 2, 2020 9:12:00 AM

Data Classification, Clustering, and Regression is part 5 of this series on Data Analysis. The focus of this article is to use existing data to predict the values of new data. 

If you missed the other posts in this series, read them here: 

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Topics: Python, Data Analysis, Data Clean

Create a Spreadsheet With Angular and Mat-Table

Posted by Mahmood H. Alnasser on Mar 31, 2020 7:30:00 AM

In this article, we are going through a complete example of how to use Angular and Angular Material to create a spreadsheet.

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Topics: JavaScript, Material Design, Angular

Data Analysis Part 4: Clean Raw Data

Posted by Alexis Vander Wilt on Mar 10, 2020 8:44:54 AM

This is part 4 of this series on Data Analysis. The focus of this article is how to take raw data and make it more suitable for analysis.

If you missed the other posts in this series, read them here: 

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Topics: Python, Data Analysis, Data Clean

Looking further into Machine Learning using Python

Posted by Dhiraj Sharan on Mar 3, 2020 8:45:00 AM

Introduction

Welcome back! Earlier, we had covered the basics of getting started with machine learning and Python. (Here is that blog if you missed that: DataScience & Machine Learning: Where to start with Python) The current blog will take the next step and introduce some ML (Machine Learning) concepts and algorithms.

Since we are going to use Python, we will stick to the sklearn Python library as our choice.

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Topics: Machine Learning, Python, Unsupervised Learning, Supervised Learning

Data Analysis Part 3: Basic Data Visualization Techniques

Posted by Alexis Vander Wilt on Feb 20, 2020 8:45:00 AM

This is part 3 of this series on Data Analysis. The focus of this article is all the different types of data visualizations and the reasons you might choose each one.

If you missed the first two pieces in this series, read them here: An Introduction to Data Analytics and  What is Data.

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Encrypted communication on Elastic server by setting up TLS and adjusting CORS permissions

Posted by Minxuan Sun on Feb 18, 2020 9:03:30 AM

In this tutorial, we will provide step-by-step instructions on how to encrypt communication among Elasticsearch, Kibana, and Logstash. After the setup, Elasticsearch and Kibana can be accessed by web browsers via https.

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Topics: Kibana, elasticsearch, encryption, Logstash

Data Analysis Part 2: What is Data

Posted by Alexis Vander Wilt on Feb 13, 2020 8:57:46 AM

This is part 2 of this series on Data Analysis. The focus of this article is all the different types of data and how each one can be analyzed.

If you have not yet read Part 1, you can do so here.

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Topics: Data Analysis, Data Analytics, AI, Classifying Data

How to set up the Kibana Plugin Development Environment

Posted by Minxuan Sun on Feb 11, 2020 11:50:37 AM

In this tutorial, we will provide step-by-step instructions on how to set up the Kibana Plugin Development Environment in the Linux system. We use Ubuntu 18.04.2 Desktop as an example.

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Topics: Ubuntu, Kibana, Data Exploration, Data Visualization

Data Analysis Part 1: An Introduction to Data Analytics

Posted by Alexis Vander Wilt on Feb 6, 2020 1:00:00 PM

Every day, words like “Artificial Intelligence”, “Big Data”, “Data Analytics”, and “Machine Learning” are thrown around constantly these days, but what do they actually mean? Perhaps more importantly, why should you care?

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Topics: Data Analysis, Data Analytics, AI