Algolytics
• Male

Algolytics's Page

Oct 9

Profile Information

Short Bio
Algolytics is software development company offering tools for Predictive Analytics, Data Quality, Social Network Analysis and other advanced data analysis tasks.

My Web Site Or LinkedIn Profile
http://algolytics.com/
Field of Expertise
Analytics, Data Integration, Visualization, BI, Other, Big Data, Data Science
Professional Status
Other
Years of Experience:
14
Algolytics
Interests:
Networking, New venture
What Other Analytical Website do you Recommend?
http://algolytics.com/blog/

Algolytics's Blog

Understanding Machine Learning: How machines learn?

Posted on April 13, 2017 at 4:00am

“If (there) was one thing all people took for granted, (it) was conviction that if you feed honest figures into a computer, honest figures (will) come out. Never doubted it myself till I met a computer with a sense of humor.”

― Robert A. Heinlein, The Moon is a Harsh Mistress

This post is the first in a series of articles in which we will explain what Machine Learning is. You don’t have to have formal training or…

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Correlation does not imply causation

Posted on December 13, 2016 at 4:30am

A popular phrase tossed around when we talk about statistical data is “there is correlation between variables”. However, many people wrongly consider this to be the equivalent of “there is causation between variables”. It’s important to explain the distinction: Correlation means that once we know how one variable changes we can make reasonable deductions about how other variables change There are several variants of correlation:

1. Positive…

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Understanding machine learning #3: Confusion matrix - not all errors are equal

Posted on November 13, 2016 at 4:30am

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let’s assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be  a good result – but is it really? Should we consider a model with 93% accuracy as adequate?

It depends. Today, we…

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Understanding machine learning: Do we need machine learning at all?

Posted on October 13, 2016 at 4:30am

In the previous post of our Understanding machine learning series, we presented how machines learn through multiple experiences. We also explained how, in some cases, human beings are much better at interpreting data than machines. In many tasks machines still can’t replace humans, who understand surrounding reality better and can make more accurate decisions.

Machines can be given a…

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