David Mimno is an assistant professor in the Information Sciences department at Cornell University. He has a background and interest in Natural Language Processing (NLP), specifically topic modeling. Notably, he is the chief maintainer of MALLET, the Java-based NLP library. I recently came across a blog post by David titled “Advice for students of machine […]
You have to get your hands dirty. You can read all of the blog posts and watch all the videos in the world, but you’re not actually going to start really get machine learning until you start practicing. The scikit-learn Python library is very easy to get up and running. Nevertheless I see a lot […]
A question I get asked a lot is: How can I make money with machine learning? You can get a job with your machine learning skills as a machine learning engineer, data analyst or data scientist. That is the goal of a great many people that contact me. There are also other options. In this […]
In this post I lay out a concrete self-study roadmap for applied machine learning that you can use to orient yourself and figure out your next step. I think a lot about frameworks and systematic approaches (as evidenced on my blog). I would consider this post a vast expansion of my previous thoughts on a self-study […]
Reading bootstrapping machine learning, Louis mentioned a paper that I had to go off and read. The title of the paper is Machine Learning that Matters (PDF) by Kiri Wagstaff from JPL and was published in 2012. Kiri’s thesis is that the machine learning research community has lost its way. She suggests that much of machine […]
Louis Dorard has released his book titled Bootstrapping Machine Learning. It’s a book that provides a gentle introduction to the field of machine learning targeted at developers and start-ups with a focus on prediction APIs. I just finished reading this book and I want to share some my thoughts. If you are interested, I have […]
Hal Varian is the chief economist at Google and gave a talk to Electronic Support Group at EECS Department at the University of California at Berkeley in November 2013. The talk was titled Machine Learning and Econometrics and was really focused on what lessons the machine learning can take away from the field of Econometrics. […]
Project layout is critical for machine learning projects just as it is for software development projects. I think of it like language. A project layout organizes thoughts and gives you context for ideas just like knowing the names for things gives you the basis for thinking. In this post I want to highlight some considerations […]
A question I get asked a lot is: What is the best programming language for machine learning? I’ve replied to this question many times now it’s about time to explore this further in a blog post. Ultimately, the programming language you use for machine learning should consider your own requirements and predilections. No one can meaningfully address […]
For as long as I have been participating in data mining and machine learning competitions, I have thought about automating my participation. Maybe it shows that I want to solve the problem of building the tool more than I want to solve the problem at hand. When working on a dataset, I typically spend a […]
