Bootstrapping Machine Learning: Book Review

Last Updated on June 7, 2016 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 already reviewed the sample Louis provides on his webpage that covers the first two chapters. Bootstrapping Machine Learning Overview […]

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Machine Learning that Matters

Last Updated on September 5, 2016 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. Machine Learning that Matters Kiri’s thesis is that the machine learning research community has lost its way. She suggests that much of machine learning is done for machine learning’s sake. She points to three key problems: Overfocus on […]

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Machine Learning with Quantum Computers

Last Updated on June 17, 2019 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 Machine Learning and I had to jump on it. The talk is by Seth Lloyd from MIT. The talk […]

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The Data Analytics Handbook: Researchers and Academics Review

Last Updated on June 7, 2016 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 book in the series contains 8 interviews with academics and researchers and is called The Data Analytics Handbook: Researchers and […]

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Data Cleaning: Turn Messy Data into Tidy Data

Last Updated on August 16, 2020 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 representations through your pipeline. Hadley Wickham is the Adjunct Professor at Rice University and Chief Scientist and RStudio and […]

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The Missing Roadmap to Self-Study Machine Learning

Last Updated on June 7, 2016 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 program in the post “Self-Study Guide to Machine Learning” that really hit a chord in the community. Let’s jump […]

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What Is Holding You Back From Your Machine Learning Goals?

Last Updated on December 24, 2016 Identify and Tackle Your Self-Limiting Beliefs andFinally 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 always it is a self-limiting belief that has halted their progress. In this post, I want to touch on some […]

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Build a Machine Learning Portfolio

Last Updated on September 27, 2016 Complete Small Focused Projects and Demonstrate Your Skills A portfolio is typically used by designers and artists to show examples of prior work to prospective clients and employers. Design, art and photography are examples where the work product is creative and empirical, where telling someone you can do it is not valued the same as showing them. In this post, I will convince you that building a machine learning portfolio has value to you, […]

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Work on Machine Learning Problems That Matter To You

Last Updated on September 27, 2016 It is difficult to stay motivated when self-studying machine learning. The standard test datasets can be quite obtuse and disconnected from you and from your everyday life. Boring even. A trick that you might like to use is to find and work on a dataset that matters to you. In this post, we will look at some ideas for datasets that you could use to motivate and even accelerate your journey into applied machine learning. […]

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Machine Learning for Money

Last Updated on September 27, 2016 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 post, I want to highlight some of those other options and try to get your gears turning. There are […]

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