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Ever wondered how most of the fastest-growing jobs in the tech sector today were not even existing a few years ago. It is surveyed that the employees are worried about the skill gap, which is restricting them from shifting to companies which offer better skill development initiative.…

This article was written by Rohith Gandhi.

Introduction

Computer vision is an interdisciplinary field that has been gaining huge amounts of traction in the recent years(since CNN) and self-driving cars have taken centre stage. Another…

For over 3 decades, my passion has been to assist organizations leverage the business potential of data and analytics and help them envision where and how data and analytics can generate new sources of customer, product and operational value. Maybe my fascination with data and analytics started in my youth with “Strat-o-Matic Baseball” but it certainly caught fire at Metaphor Computers in the 1980’s…

Face recognition is a combination of two major operations: face detection followed by Face classification. In this tutorial, we will look into a specific use case of object detection – face recognition.

The pipeline for the concerned project is as follows:

1. Face detection: Look at an image and find all the possible faces in it
2. Face extraction: Focus on each…

Aided by the availability of vast amounts of data computing resources, machine learning (ML) has made big strides. The financial industry, which at its heart is an information processing enterprise, holds an enormous amount of opportunity for the deployment of these new technologies.

Machine Learning for Finance is a practical guide to modern ML applied in the financial industry. This book is not only about investing or trading in the finance sector;…

How does one measure the effectiveness of a new Augmented Data Discovery solution? Once the business has chosen data democratization and implemented a self-serve analytics solution, it must measure ROI & TCO and establish metrics that will compare business results achieved before and after the implementation.

Without measurable results, it…

The basic idea behind regression analysis is to take a set of data and use that data to make predictions. A useful first step is to make a scatter plot to see the rough shape of your data. Then, choose a regression method to…

The “Big data” is in vogue these days. Most of the people who are aware of the term state that big data is a source of power and can bring about drastic revolutions in the scores of some of the major industrial sectors. However, the tools available that bring about changes in big businesses and small leading to the Big Data Revolution are known to a very few. Here you can get a sneak-peek of the tools that are available and how the tools fit into a…

Note: This blog was written by Dr. John Elder and was originally published on www.elderresearch.com/blog.

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Elder Research has solved many challenging and previously unsolved technical problems in a wide variety of fields for…

Note: This blog was written by Dr. William Goodrum and originally posted on www.elderresearch.com.

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It has become common to talk about data being the new oil. But a recent piece from …

I enjoy data prep munging for analyses with computational platforms such as R, Python-Pandas, Julia, Apache Spark, and even relational databases. The wrangling cycle provides the opportunity to get a feel for and preliminarily explore data that are to be later analyzed/modeled.

A critical task I prefer handling in computation over database is…

I have been frequently asked about the tools for the Machine Learnign projects There are lot of them on the market so in my newest post you will find my view on them. I would like to start my first Machine Learning project. But I do not have tools. What should I do? What are the tools I could use?

I will give you some hints and advices based on the toolbox I use. Of course there are more great tools but you should pick the ones you like. You should also use the tools that make your…

I have mentioned that every Machine Learning process is built from several steps like:

• what would you like to achieve (define the goal)
• prepare the data
• select an algorithm(s)
• build and train the model
• test the model (and score it)

Let’s review them one by one. I should mention that you will be able to find information over the internet that the number of steps is different from what you see on this blog. For example you can…

What is Machine Learning? This story started in mid 60 in the last century. Scientists and engineers found a lot of problems that were too complicated for traditional algorithms that it was not possible to create a program that could have possibly solved the problem.

Imagine a case where you have an object that is descirbed by 21 properties (as an input) and based on these properties you need to classify it to group A or to the group B. How would you solve the problem? Would you need…

Summary:  Move over RNN/LSTM, there’s a new algorithm called Calibrated Quantum Mesh that promises to bring new levels of accuracy to natural language search and without labeled training data.

There’s a brand new algorithm for natural language search (NLS) and natural language understanding (NLU) that not…

Deploying Natural Language Processing for Product Reviews

When I first heard about machine learning (ML), I thought only big companies applied it to explore big data. On searching the internet for the meaning of ML, I discovered that Wikipedia defines it as a subset of artificial intelligence (AI). In particular, it involves the scientific study of algorithms and statistical models that computer systems use to…

Decision Trees, Random Forests and Boosting are among the…

Originally posted by Vincent Ajayi. 

The most common challenge faced by data scientists (DS) and data analysts (DA) is missing data. Every day, both DA and DS spend several hours dealing with missing data. The question is why is missing data a problem? Analysts presume that all variables should have a particular value at a particular state, and when there is no value for the…