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July 2019 Blog Posts (102)

Why Every Data Science Aspirant Should Learn Data Visualization

[Image Source - Freepik]

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.…

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Added by Dave Jarvis on July 31, 2019 at 8:56pm — No Comments

R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms

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…

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Added by Andrea Manero-Bastin on July 31, 2019 at 11:00am — No Comments

What is “The Art of Thinking Like a Data Scientist” Workbook and Why It Matters

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…

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Added by Bill Schmarzo on July 31, 2019 at 10:24am — 3 Comments

Building Face Recognition using FaceNet

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…
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Added by Packt Publishing on July 31, 2019 at 5:38am — No Comments

Discover how machine learning can solve finance industry challenges by Jannes Klaas

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…

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Added by Packt Publishing on July 31, 2019 at 5:23am — No Comments

How Do I Prove The Value Of Self-Serve Augmented Data Discovery?

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…

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Added by Kartik Patel on July 31, 2019 at 4:20am — No Comments

Regression Analysis in One Picture

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.…

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Added by Stephanie Glen on July 31, 2019 at 4:00am — No Comments

Introductory Guide to Data Science

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…

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Added by Nisha Dhiman on July 30, 2019 at 11:00pm — No Comments

Investment Modeling Grounded In Data Science

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…

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Added by Paul Derstine on July 30, 2019 at 9:30am — No Comments

Data is Not Oil. It is Land.

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 …

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Added by Paul Derstine on July 30, 2019 at 9:30am — No Comments

Using Python and R to Load Relational Database Tables, Part I

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…

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Added by steve miller on July 30, 2019 at 6:34am — 1 Comment

The tools you should know for the Machine Learning projects

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…

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Added by Damian Widera on July 29, 2019 at 7:30pm — No Comments

What does the Machine Learning process look like?

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…

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Added by Damian Widera on July 29, 2019 at 7:30pm — No Comments

Introduction to Machine Learning

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…

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Added by Damian Widera on July 29, 2019 at 7:30pm — No Comments

Calibrated Quantum Mesh – Better Than Deep Learning for Natural Language Search

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…

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Added by William Vorhies on July 29, 2019 at 8:02am — No Comments

Natural Language Processing

Deploying Natural Language Processing for Product Reviews

Introduction

We have data all around us and there are of two forms of data namely; tabular and text. If you…

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Added by Neeraj on July 29, 2019 at 7:30am — No Comments

Common Applications of Machine Learning for Small-Scale Businesses

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…

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Added by Vincent O Ajayi on July 29, 2019 at 2:30am — No Comments

7 Steps to Ensure and Sustain Data Quality

Several years ago,  I met a senior director from a large company.  He mentioned the company he worked for was facing data quality issues that eroded customer satisfaction, and he had spent months investigating the potential causes and how to fix them. “What have you found?” I asked eagerly.  “It is a tough issue.  I did not find a single cause, on the contrary, many things went wrong,” he replied.  He then started citing a long list of what contributed to the data quality issues - almost…
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Added by Stephanie Shen on July 28, 2019 at 3:30pm — No Comments

How to deal with missing data

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…

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Added by Vincent Granville on July 27, 2019 at 7:25pm — No Comments

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