How Big Data is used in Recommendation Systems to change our lives
A Recommendation systems have impacted or even redefined our lives in many ways. It works in well-defined, logical phases which are data collection, ratings, and filtering.
on Oct 30, 2015 in Amazon, Big Data, Kaushik Pal, Recommendations, Recommender Systems
Integrating Python and R, Part 2: Executing R from Python and Vice Versa
The second in a series of blog posts that: outline the basic strategy for integrating Python and R, we will concentrate on how the two scripts can be linked together by getting R to call Python and vice versa.
on Oct 30, 2015 in Python, Python vs R, R
Integrating Python and R into a Data Analysis Pipeline, Part 1
The first in a series of blog posts that: outline the basic strategy for integrating Python and R, run through the different steps involved in this process; and give a real example of how and why you would want to do this.
on Oct 29, 2015 in Data Analysis, Mango Solutions, Python, Python vs R, R
We need a statistically rigorous and scientifically meaningful definition of replication
Replication and confirmation are indispensable concepts that help define scientific facts. It seems that before continuing the debate over replication, we need a statistically meaningful definition of replication.
on Oct 29, 2015 in Replication, Reproducibility, Statistics
Data Science of IoT: Sensor fusion and Kalman filters, Part 1
The Kalman filter has numerous applications, including IoT and Sensor fusion, which helps to determine the State of an IoT based computing system based on sensor input.
on Oct 29, 2015 in FutureText, IoT, Kalman Filters, Sensors
Amazon Top 20 Books in Data Mining
These are the most popular data mining books on Amazon. As you look to increase your knowledge, is there something listed here that is missing from your collection?
on Oct 27, 2015 in Amazon, Book, Data Mining
Random vs Pseudo-random – How to Tell the Difference
Statistical know-how is an integral part of Data Science. Explore randomness vs. pseudo-randomness in this explanatory post with examples.
on Oct 26, 2015 in Correlation, Random
Cartoon: KDnuggets Addiction
New Cartoon looks at a serious case of KDnuggets addiction and what can be done about it.
on Oct 24, 2015 in About KDnuggets, Cartoon
The Data Science Machine, or ‘How To Engineer Feature Engineering’
MIT researchers have developed what they refer to as the Data Science Machine, which combines feature engineering and an end-to-end data science pipeline into a system that beats nearly 70% of humans in competitions. Is this game-changing?
on Oct 22, 2015 in Automated, Data Science, Feature Engineering, Feature Extraction, MIT
MetaMind Mastermind Richard Socher: Uncut Interview
In a wide-ranging interview, Richard Socher opens up about MetaMind, deep learning, the nature of corporate research, and the future of machine learning.
on Oct 20, 2015 in Convolutional Neural Networks, Deep Learning, Image Recognition, MetaMind, Recurrent Neural Networks, Richard Socher, Zachary Lipton
Infographic – Data Scientist or Business Analyst? Knowing the Difference is Key
Infographic depicting unique differences between data scientists and business analysts. Find out what type of professional is needed to meet your organization’s needs.
on Oct 20, 2015 in Business Analyst, Data Scientist, Education, Infographic, Jobs
Which Movie Sequels Are Really Better? A Data Science Answer
The internet is filled with polls and lists of sequels that are better or worse movie in the series. Yet such rankings are often based on personal judgement and rarely on data and statistics. Here is our solution to analyze and visualize the movie series.
on Oct 19, 2015 in Data Analysis, Data Visualization, IMDb, James Bond, Movies, Silk.co
The Best Advice From Quora on ‘How to Learn Machine Learning’
Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information.
on Oct 15, 2015 in Books, Machine Learning, Matthew Mayo, MOOC, Quora, Sean McClure, Xavier Amatriain
Aspect Based Sentiment Analysis Competition
SemEval is back and so is the Aspect Based Sentiment Analysis (ABSA) competition, which has gone multilingual for ABSA16. Get all of the details below.
on Oct 13, 2015 in Competition, Sentiment Analysis
Does Deep Learning Come from the Devil?
Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
on Oct 9, 2015 in Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton
Online course: Credit Risk Modeling
The course covers basic and advanced modeling, including stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.
on Oct 7, 2015 in Bart Baesens, Credit Risk, Online Education, Risk Modeling
Recurrent Neural Networks Tutorial, Introduction
Recurrent Neural Networks (RNNs) are popular models that have shown great promise in NLP and many other Machine Learning tasks. Here is a much-needed guide to key RNN models and a few brilliant research papers.
on Oct 7, 2015 in Deep Learning, Neural Networks, NLP, Recurrent Neural Networks
How big data can help in home health care?
Proper home care services can reduce both the chances and the cost of hospitalization and manage illness. Understand what big data promises for the healthcare sector and what are practical hurdles standing between the current solutions.
on Oct 7, 2015 in Big Data, Healthcare, Kaushik Pal
Top /r/MachineLearning Posts, September: Implement a neural network from scratch in C++
Neural network in C++ for beginners, Chinese character handwriting recognition beats humans, a handy machine learning algorithm cheat sheet, neural nets versus functional programming, and a neural nets paper repository.
on Oct 6, 2015 in C++, Deep Learning, Matthew Mayo, Neural Networks, Python, R, Reddit
Crushed it! Landing a data science job
Data scientist interviews depend on the company and the team, it might look like a software developer’s interview, or statistician’s interview. Here we collected some hot tips to pass along if you’re thinking about a move soon.
on Oct 1, 2015 in Data Scientist, Erin Shellman, Hiring, Interview
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