So, what I’m about to write about actually occurred a few months ago, but I am reminded of it every day when I receive an email from Data Science Central or see someone tweet an article from the blog network (which includes Analytics Bridge, Big Data News, etc.), so I figured if it’s still bothering me, it’s worth writing about.
Doing Data Science (Review)
I just finished reading Doing Data Science: Straight Talk from the Frontline, an O’Reilly book by Cathy O’Neil (@mathbabedotorg) and Rachel Schutt (Columbia Data Science blog). First let me say, I really enjoyed this book! I thought it gave a great overview of Data Science, which is very valuable at this early stage in my… Continue reading Doing Data Science (Review)
The Signal and the Noise (Review)
This is a review of The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t by Nate Silver.
Goal #1 Reached!
My first “Becoming A Data Scientist” goal was to get an “A” in my Machine Learning class this semester, and I did! Now I can cross that one off the list: Updated Goals
ML Project 4 Results
I am happy to report that I got 100% on the final project I did in the last 2 weeks for my Machine Learning grad class (which is especially great because that was 30% of my grade for the semester!) and I got some good feedback from the professor: Very good analysis and you showed… Continue reading ML Project 4 Results
Machine Learning Project 4
So immediately after I turned in project 3, I started on Project 4, our final project in Machine Learning grad class. We had a few options that the professor gave us, but could also propose our own. One of the options was learning how to implement Random Forest (an ensemble learning method using many decision trees) and analyzing a given data set, so I proposed using Random Forest on University Advancement (Development/Fundraising) data I got from my “day job”. The professor approved it, so I started learning about Random Forest Classification.
ML Projects 2 & 3 Results
I was in such a rush to finish Project 3 by Sunday night, I didn’t post about the rest of my results, and now before I got a chance to write about it, the professor has already graded it! I got 100% on this one I just turned in, and also just found out I got 100% on Project 2!! This makes me feel so good, especially since I didn’t do so well on the midterm, and confirms that I can do this!
ML Project 3 (Post 3)
Last night I got the FNN Classifier working on the 16-input 10-output data file for Project 3 in my Machine Learning class.
Here’s the output!
ML Project 3 (Post 2)
Tonight I have learned how to use PyBrain’s Feed-Forward Neural Networks for Classification. Yay! I had already created a neural network and used it on the project’s regression data set earlier this week, then used those results to “manually” classify (by picking which class the output was closer to, then counting up how many points… Continue reading ML Project 3 (Post 2)
Machine Learning Project 3
I’m in the midst of working on Project 3 for my Machine Learning class. This one has the following tasks: Train a 3-layer (input, hidden, output) neural network with one hidden layer based on the given training set which has 8 inputs and 7 outputs. Obtain training & testing errors with the number of hidden… Continue reading Machine Learning Project 3