Machine learning algorithms are parameterized so that they can be best adapted for a given problem. A difficulty is that configuring an algorithm for a given problem can be a project in and of itself.
Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for a problem. The best thing to do is to investigate empirically with controlled experiments.
The caret R package was designed to make finding…Continue
Added by Diego Marinho de Oliveira on April 7, 2015 at 6:41am — No Comments
Authors: Michael J. Lopez / Gregory J. Matthews.Continue
Journal of Quantitative Analysis in Sports. Volume 11, Issue 1, Pages 5–12.
Abstract Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts…
Added by Diego Marinho de Oliveira on April 7, 2015 at 12:46am — No Comments
Good day! Sharing a news –
CERN has published an Open Data Portal where you can play with their data for education or research. And, it actually has data from experiments of the Large…Continue
Added by Mohammad Oli Ahad on November 25, 2014 at 3:03am — No Comments
This exercise was done to understand the software skills that are in high demand for Data Science. Analysis was done by extracting the job postings from popular online websites. The findings are interesting. R continues to be the most popular skill, found in 70% of the postings. Python follows as a close second. Surprisingly, in spite all the talk about "Big Data Science", SQL comes up third. This shows that traditional RDBMS still continue to be the base for machine learning work…Continue
From episode 10 of my Naked Analyst Channel on YouTube.
I think I do - and it is the ‘appification’ of analytics. What I mean by this is the reduction of a complex analytic activity such as market segmentation, down to a single button on your computer interface. Very much like the…Continue
Added by derick.jose on June 17, 2014 at 2:00am — No Comments
We establish understanding of things in terms of Data or it will be better to say in terms of Big Data, the utilization of things, matters, issues, inventions, surroundings, maps and much more throughout our everyday life cycle, all of which has a certain data type to get input, process and output for us. Sometime we understand these in almost no time as a human, where data is being originated, what are we targeting for and more, and there are times, when some thing might take longer…Continue
Added by Atif Farid Mohammad on November 29, 2013 at 12:50am — No Comments
Smart organizations are using the power of data science and data produced by embedded sensors and machine devices to better measure performance, discover patterns, prevent problems, and improve…Continue
Practicing Data science indeed a long term effort than a learning handful of skills. We ought to be academically good enough to take up this challenge. However, if you think you came a long way from your academic rebuilding, but you still have that zeal & passion to take the oil from the data and fill the skill gap of data science then here is the warm-up tips. Below points must exercised before jumping into…Continue
Added by Manish Bhoge on October 18, 2013 at 9:26am — No Comments
Text (word) analysis and tokenized text modeling always give a chill air around ears, specially when you are new to machine learning. Thanks to Python and its extended libraries for its warm support around text analytics and machine learning. Scikit-learn is a savior and excellent support in text processing when you also understand some of the concept like "Bag of word", "Clustering" and "vectorization". Vectorization is must-to-know technique for all machine leaning learners, text miner…Continue
Added by Manish Bhoge on September 25, 2013 at 9:47am — No Comments
Natural language processing (NLP) involves machine learning, artificial intelligence, algorithms and linguistics related to interactions between computers and human languages. One important goal…Continue
Added by Michael Walker on August 20, 2013 at 7:27pm — No Comments
Machine Learning: A Probabilistic Perspective, by Kevin Murphy.
Boosting: Foundations and Algorithms, by Robert E. Schapire.
Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, by Emanuel Derman.
Doing Data Science, by Cathy O'Neil and Rachel…
M2M + Big Data Analytics = Unlocking Blue Ocean Opportunities at the intersect
When human beings got connected, it unlocked a whole new set of possibilities and companies like facebook, linkedin etc came up with solutions which had never been there before.
The ability of machines to interact with each other promises to unlock a whole new set of opportunities unprecedented in our history
What are some real life examples of M2M…Continue
We have tried to synthesize the most disruptive big data use cases into a compact . 3 Minute video
It covers 6 use cases , 4 healthcare data streams and hopefully sets the stage for curating more use cases in an area which truly needs a lot of healthy transformations !!!
Added by derick.jose on June 5, 2013 at 10:20am — No Comments
I found it odd there was no way to automatically deskew data in R, so I wrote a short little function to do it. It noticeably improves the peformance of linear models and linear support vector machines.