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Machine Learning Showcase

Feb 26

Realtime Customer Feedback Analysis with IBM Watson Natural Language Classifier

By Shyam Purkayastha | Machine Learning

With an ever-changing business landscape and diverse user base, it is all the more important to keep a close eye your customer’s perception. As Peter Drucker famously said, “what gets measured gets improved.”

Customer interactions with your business take the form of various channels, most of them being indirect. If you can figure out a quantifiable way to measure your customer’s feedback, then you have got every chance of improving your business.

Sep 11

How To Leverage Machine Learning in Retail Stores for Automated Stock Replenishment

By Priyabrata Dash | Machine Learning

Have you ever wondered, how does a retail store keep track of its shelves? A retail store spread across a large area requires a lot of operational staff to manage the shelves. Replenishing the stock is one of those essential chores that ensure that the business runs without any halt. In this blog post, we are going to talk about how technology can help in predicting when does a store shelf need to replenish itself. We are going to showcase automated stock replenishment as a case scenario for applying machine learning in retail industry.   

Jul 07

Build an Interactive and Visually Driven Online Campaign with Watson Visual Recognition

By Shyam Purkayastha | Machine Learning

There are many occasions where visual content delivers more engaging and enriching experiences than the typical data-driven approach. Using IBM Watson Visual Recognition service, you can analyze and extract objects from images to gather quantitative data.

In this tutorial, you’ll leverage Watson Visual Recognition to build a worldwide campaign to experience the power of visually-driven data stories. The tutorial will show you how to leverage the IBM Watson Visual Recognition capabilities to build an interactive, image-driven storyline.

May 11

Beat the Rush Hour Traffic with Machine Learning using Tensorflow

By Shyam Purkayastha | Machine Learning

TensorFlow is one of Google’s flagship machine learning toolkits. Originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization, Tensorflow found adoption in conducting machine learning and deep neural networks research. Subsequently, it was open sourced and is now available as a general purpose ML toolkit for a wide variety of applications. In this blog post, we will show you how to use the kNN routines of Tensorflow to solve a fundamental problem of city commuters, the traffic jams.

Feb 22

Build a real-time recommendation engine with IBM Bluemix and PubNub – Part 2

By Shyam Purkayastha | Machine Learning

This post is the follow up to my previous post in which I presented a real-time recommendation engine project around predicting duration of travel. In that post, I explored the factors affecting travel duration and looked at how, with the help of historical data, you can build an algorithm for predicting travel duration for a given route. Now, I will conclude the project by building an application that can help users plan a trip by providing real-time travel recommendations based on current traffic and weather conditions.

Feb 22

Build a real-time recommendation engine with IBM Bluemix and PubNub – Part 1

By Shyam Purkayastha | Machine Learning

Image, Courtesy 123RF. Copyright: alfaphoto / 123RF Stock Photo

In this two-part blog series, I am going to show you how to build a real-time recommendation engine for advising on travel decisions. Travel time or duration is something that is always subject to a lot of speculation. Based on your travel experience along a particular route, you always bet on your intuition to decide when to start your journey. But if you are planning to travel for an all-important meeting to close a deal, a job interview that you have to crack at any cost, or a public event that you have to witness from the very beginning, then you better be prepared and alert, but how?

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