Deep Learning, NLP, NMT, AI, ML
Last Updated on June 10, 2019 I get a lot of emails asking about how I got interested in machine learning and about my background. I don’t think my story is special or interesting, but I’m happy to share it and honored I’m asked. This post feels a little self-indulgent. I figure it can be the definitive version of my story that I can use to answer similar inquiries in the future. The lessons that I point to underly much […]
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Last Updated on June 7, 2016 A question that comes up from time to time is: What hardware do I need to practice machine learning? There was a time when I was a student when I was obsessed with more speed and more cores so I could run my algorithms faster and for longer. I have changed my perspective. Big hardware still matters, but only after you have considered a bunch of other factors. TRS 80!Photo by blakespot, some rights […]
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Last Updated on August 12, 2019 What is the best machine learning algorithm? I get this question a lot. Maybe even daily. Sometimes it’s a general question. I figure people want to make sure they are learning the one true machine learning algorithm and not wasting their time on anything less. Most other times it is with regard to a specific problem. I think it’s a very good question, a very telling question. It tells me straight away that a […]
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Last Updated on August 21, 2019 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 of hesitation from beginners looking get started. In this blog post I want to give a few very simple […]
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Last Updated on August 15, 2020 If you are using the Python stack for studying and applying machine learning, then the library that you will want to use for data analysis and data manipulation is Pandas. This post gives you a quick introduction to the Pandas library and point you in the right direction for getting started. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all […]
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Last Updated on January 28, 2020 Before you can select and prepare your data for modeling, you need to understand what you’ve got to start with. If you’re a using the Python stack for machine learning, a library that you can use to better understand your data is Pandas. In this post you will discover some quick and dirty recipes for Pandas to improve the understanding of your data in terms of it’s structure, distribution and relationships. Kick-start your project […]
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Last Updated on August 16, 2020 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 learning“. This is a great post and includes similar advice that I give to programmers and coaching students. It’s […]
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Last Updated on August 16, 2020 R is a powerful platform for data analysis and machine learning. It is my main workhorse for things like competitions and consulting work. The reason is the large amounts of powerful algorithms available, all on the one platform. In this post I want to point out some resources you can use to get started in R for machine learning. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials […]
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Last Updated on June 7, 2016 Online communities are invaluable in machine learning, regardless of your skill level. The reason is that, like programming, you never stop learning. You simply cannot know everything, there are always new algorithms, new data and new combinations to discover and practice. Communities help. You can get your questions answered, learn by answering other peoples questions and discover new areas from reading through the exchanges. Machine learning communities have had a big impact on my […]
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Last Updated on September 5, 2016 Julia Evans wrote a post recently titled “Machine learning isn’t Kaggle competitions“. It was an interesting post because it pointed out an important truth. If you want to solve business problems using machine learning, doing well at Kaggle competitions is not a good indicator of that skills. The rationale is that the work required to do well in a Kaggle competition is only a piece of what is required to deliver a business benefit. […]
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