We are all familiar with machine learning in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better shopping and movie experience.
Artificial intelligence (AI) has stormed the world today. It is an umbrella term that includes multiple technologies, such as machine learning, deep learning, and…
Added by Sandeep Raut on August 27, 2017 at 3:00am — No Comments
This article was written by Prashant Gupta. Gupta is a machine learning engineer, Android developer, and tech enthusiast.
Tired of getting low accuracy on your machine learning models? Boosting is here to help. Boosting is a popular machine learning algorithm that increases accuracy of…Continue
Added by Amelia Matteson on August 25, 2017 at 2:30pm — No Comments
Over the last few years, organizations have made a strategic decision to turn big data into competitive advantage. Owing to rapid changes in the trends of BI and DW space, Big Data has been driving the organizations to explore the implementation aspects on how to integrate big data into the existing EDW infrastructure. The process of extracting data from multiple sources such as social media, weblogs, sensor data etc. and transforming that data suit the organization’s analytical needs is…Continue
Guest blog post by Mic Farris. Mic is a Decision Science & Analytics Leader at CenturyLink.
Two of the biggest buzzwords in our industry are “big data” and “data science”. Big Data seems to have a lot of interest right now, but Data Science is fast becoming a very hot topic.
Source for picture: …Continue
Connecting the dots for a Deep Learning App … Our day to day activities is filled with Emotions and Sentiments. Ever wondered how we can identify these sentiments through computers? Oops, computers who have no brains :)? Try this Deep Learning App yourself (refresh a couple of times initially if there’s Application Error):
Dot 0: Deep Learning in Sentiment Analysis
Added by Janardhan Shetty on August 24, 2017 at 7:00am — No Comments
Here is our selection of featured articles and resources posted since Monday
One of the most intuitive and popular methods of data mining that provides explicit rules for classification and copes well with heterogeneous data, missing data, and nonlinear effects is decision tree. It predicts the target value of an item by mapping observations about the item.
You can perform either classification or regression tasks here. For example, identifying fraudulent…Continue
This article was written by Adit Deshpande.
This is the 3rd installment of a new series called Deep Learning Research Review.
Introduction to Natural Language Processing:
Added by Amelia Matteson on August 23, 2017 at 2:30pm — No Comments
In this rapidly changing world of technology, chatbots market is projected to show major growth prospects during the forecast period. The growing need to maintain a healthy customer relationship management is a major factor driving the growth of chatbots market. Chatbots enables companies to engage in continuous communication with their customers which results in improvement of customer relationship management, which is the ultimate goal of any…Continue
Added by Sagar kadam on August 23, 2017 at 1:30am — No Comments
The sudden increase in the volume of data from the order of gigabytes to zettabytes has created the need for a more organized file system for storage and processing of data. The demand stemming from the data market has brought Hadoop in the limelight making it one of biggest players in the industry. Hadoop Distributed File System (HDFS), the…Continue
Added by Noah Data on August 22, 2017 at 9:00pm — No Comments
Nonlinear Regression and Generalized Linear Models:
Regression is nonlinear when at least one of its parameters appears nonlinearly. It commonly sorts and analyzes data of various industries like retail and banking sectors. It also helps to draw conclusions and predict future trends on the basis of user’s activities on the net.
The nonlinear regression analysis is the process of building a…
Added by Shreya Gupta on August 22, 2017 at 9:00pm — No Comments
This article was written by Nathan Yau.
It used to be that we’d see a poorly made graph or a data design goof, laugh it up a bit, and then carry on. At some point though — during this past year especially — it grew more difficult to distinguish a visualization…
Added by Amelia Matteson on August 22, 2017 at 4:30pm — No Comments
First came an infographic about 12 careers in big data. Here is another interesting one from our guest blogger Chetan Ramesh…Continue
Added by Shay Pal on August 22, 2017 at 9:00am — No Comments
Added by Shay Pal on August 22, 2017 at 9:00am — No Comments
Summary: Reinforcement Learning (RL) is likely to be the next big push in artificial intelligence. It’s the core technique for robotics, smart IoT, game play, and many other emerging areas. But the concept of modeling in RL is very different from our statistical techniques and deep learning. In this two part series we’ll take a look at the basics of RL models, how they’re built and used. In the next part, we’ll address some of the complexities that make development a…Continue
Added by William Vorhies on August 22, 2017 at 9:00am — No Comments
Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some domain knowledge of the discipline where the data has originated from. For example, if one is dealing with signals (i.e. classification of EEG signals), then possible features would involve…Continue
All of us are accustomed to the smart wearables, such as the ones we wear on a jogging track. We also have seen the concept of smart homes turn into a reality. We have seen a farmer sort and track his flock of sheep with the help of a mountable RFID device.
Every physical element around us (including ourselves) have become a part…Continue
Added by Ronald van Loon on August 22, 2017 at 2:00am — No Comments
Here we describe a rudimentary method, using basic trigonometry, to predict lunar and solar eclipses. The purpose is to get people interested in the mathematics behind these events. We assume here that the Sun, the Earth and the Moon are just points. Thus we do not predict where on Earth eclipses take place, nor whether they are full of partial. But we derive some interesting results, such as
Added by Vincent Granville on August 21, 2017 at 11:00pm — No Comments
Guest blog by Rob Kabacoff. Rob is Professor of Quantitative Analytics at Wesleyan University.
R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who…Continue
Added by L.V. on August 21, 2017 at 10:00am — No Comments