60% off #Practical Data Science: Reducing High Dimensional Data in R – $10

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In this R course, we’ll see how PCA can reduce a 5000+ variable data set into 10 variables and barely lose accuracy!

All Levels,  –   2.5 hours,  11 lectures 

Average rating 4.5/5 (4.5 (40 ratings) Instead of using a simple lifetime average, Udemy calculates a course’s star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.)

Course requirements:

Some understanding and interest in the R programming language

Course description:

In this R course, we’ll see how PCA can reduce a 5000+ variable data set down to 10 variables and barely lose accuracy! We’ll look at different ways of measuring PCA’s effectiveness and other ways of reducing wide data sets (those with lots of features/variables). We’ll also look at the advantages and disadvantages with different ways of reducing data.
Understand various ways of reducing wide data sets
Understand Principal Component Analysis (PCA)
Control, tune and measure the effects of PCA
Use GBM modeling to measure the effectiveness of PCA
Reducing dimensionality with classic GBM & GLMNET Variable Selection
Use ensembling techniques to find the most stable variables

Full details
Some understanding and interest in the R programming language
Interest in reducing large data sets

Reviews:

“Pros: covers software implementation of PCA
Cons: lacks a discussion of even the basic theoretical concepts, course doesn’t have graphical elements – just screen shots of the lecture notes he’s reading from.” (David Engler)

“Super course. Highly recommended.” (Dennis Marinus Scholtus)

“A great course. I can’t recall ever having taken such an informative and practical course on a computing-related topic.” (Steven Buss)

 

 

About Instructor:

Manuel Amunategui

I am data scientist in the healthcare industry. I have been applying machine learning and predictive analytics to better patients lives for the past 3 years. Prior to that I was a developer on a trading desk on Wall Street for 6 years. On the personal side, I love data science competitions and hackathons – people often ask me how can one break into this field, to which I reply: ‘join an online competition!’

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Practical Data Science: Reducing High Dimensional Data in R
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