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Maiia Bakhova
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  • San Diego
  • United States
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Short Bio
Mathematics PhD seeking a Data Scientist position at a progressive tech company. I have diverse data-manipulation experience critical to answering thoughtful scientific and marketplace questions. I apply a thorough and creative perspective to problem solving, and readily develop new skills to address yet unanswered problems. I am proficient in multiple computer languages, have experience presenting and teaching to diverse audiences, and am passionate about big data technologies.
Here is my data science blog:
http://myabakhova.blogspot.com
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/myabakhova
Field of Expertise
Analytics, Visualization, Big Data, Data Science
Professional Status
Other
Your Job Title:
Data Scientist
How did you find out about DataScienceCentral?
linkedin.com
Interests:
Finding a new position, Networking

Maiia Bakhova's Blog

Neural Networks as a Corporation Chain of Command

Posted on June 26, 2017 at 10:00am 2 Comments

Neural networks are considered complicated and they are always explained using neurons and a brain function. But we do not need to learn how to brain works to understand Neural networks structure and how they operate. We can look as something people encounter in everyday life more…

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Detection of Practical Dependency of Variables with Confidence Intervals

Posted on November 2, 2016 at 11:30am 0 Comments

This is an article which attempts to detect dependable variables with non-linear method.

I'm going to apply a method for checking variable dependency which was introduced in my previous post. Because the "dependency" I get with this rule is not true dependency as defined in Probability then I will call variables practically dependent at a confidence level…

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Measuring Dependence of Variables with Confidence Intervals.

Posted on September 6, 2016 at 1:07pm 0 Comments

In this post I will sometimes use a term “variable” for “feature”(“predictor”“) or”outcome“(”predicted value“”).

The question of variable dependencies for a particular data is quite important, because it can help to reduce an amount of predictors used for a model. Or it can tell us what feature is not helpful for a model construction, although it still can be used for engineering of another predictor. For example sometimes it is better to compute speed than to use distance values. In…

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Visualizing Bagged Trees as Approximating Borders

Posted on May 18, 2016 at 2:12pm 0 Comments

The bagged trees algorithm is a commonly used classification method. By resampling our data and creating trees for the resampled data, we can get an aggregated vote of classification prediction. In this blog post I will demonstrate how bagged trees work visualizing each step.…

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At 6:06am on February 22, 2016, Scott Sobel said…

Sure, I will do that. Thank you for your insights...

 
 
 

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