Identify and Tackle Your Self-Limiting Beliefs and Finally Make Progress I get a lot of email from developers and students looking to get started in machine learning. The first question I ask them is what is stopping them from getting started? I try to get to the heart of what they are struggling with, and almost […]
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 […]
Data preparation is difficult because the process is not objective, or at least it does not feel that way. Questions like “what is the best form of the data to describe the problem?” are not objective. You have to think from the perspective of the problem you want to solve and try a few different […]
What is the difference between a Data Analyst and a Data Scientist. This question is considered from the perspective of researchers and academics in the third instalment in the series of The Data Analytics Handbook. The first book contained 7 interviews with working analysts and data scientists. The second book contained 9 interviews with CEOs and managers. This third […]
I recently watched a Google Tech Talk with Eric Ladizinsky who visited the Quantum AI Lab at Google to talk about his D-Wave quantum computer. The talk is called Evolving Scalable Quantum Computers and is great, I highly recommend it. I’ve had quantum computing on my mind and another tech talk went by titled Quantum […]
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. […]
In a previous blog post we looked at the ebook of interviews with data analysts and data scientists put together by Liou, Tao and Lin. In this blog post we look at the second book in the series titled The Data Analytics Handbook CEOs and Managers. What are managers looking for in a Data Analyst and […]
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 […]
