Machine Learning

Machine Learning With Statistical And Causal Methods

Last Updated on September 5, 2016 In November 2014, Bernhard Scholkopf was awarded the Milner Award by the Royal Society for his contributions to machine learning. In accepting the award, he gave a layman’s presentation of his work on statistical and causal machine learning methods titled “Statistical and causal approaches to machine learning“. It’s an excellent one hour talk and I highly recommend that you watch it. Statistical Learning On the statistical side, Scholkopf talks about empirical inference and generalisation. An interesting and motivating point he makes early […]

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Machine Learning

Linear Algebra for Machine Learning

Last Updated on August 15, 2020 You do not need to learn linear algebra before you get started in machine learning, but at some time you may wish to dive deeper. In fact, if there was one area of mathematics I would suggest improving before the others, it would be linear algebra. It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms. In this […]

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Machine Learning

Common Pitfalls In Machine Learning Projects

Last Updated on June 7, 2016 In a recent presentation, Ben Hamner described the common pitfalls in machine learning projects he and his colleagues have observed during competitions on Kaggle. The talk was titled “Machine Learning Gremlins” and was presented in February 2014 at Strata. In this post we take a look at the pitfalls from Ben’s talk, what they look like and how to avoid them. Machine Learning Process Early in the talk, Ben presented a snap-shot of the process for working […]

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Machine Learning

What To Do During Machine Learning Model Runs

Last Updated on June 7, 2016 There was a recent question that asked “How to not waste-time/procrastinate while ml scripts are running?“. I think this is an important question. I think answers to this question show a level of organization or maturity in your approach to work. I left a small comment on this question, but in this post I elaborate on my answer and give you a few perspectives on how to consider this question, minimize it and even […]

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Machine Learning

Get Started and Make Progress in Machine Learning

Last Updated on June 7, 2016 In this post I layout my manifesto for how you can get started and make progress in machine learning. In this post you will discover what machine learning is, why it matters, how to do it and how to identify and overcome your self-limiting beliefs. Get Started and Make Progress in Machine LearningPhoto by Sam Howzit, some rights reserved Why Get Started in Machine Learning I believe machine learning is an important and fascinating field. You can […]

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Machine Learning

Get Paid To Apply Machine Learning

Last Updated on September 27, 2016 The Ladder Approach That You Can Use To Become aMachine Learning Consultant Do you want to do machine learning and get paid for it? Be careful what you wish for. In this post I outline a blueprint that you can use to learn enough machine learning to help small businesses and start-ups with their general data needs. It’s not easy, you will have to work hard outside of your comfort zone. You will have […]

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Machine Learning

Machine Learning for Developers

Last Updated on May 18, 2020 How Do I Get Started In Machine Learning? I’m a developer. I have read a book or some posts on machine learning. I have watched some of the Coursera machine learning course. I still don’t know how to get started… Does this sound familiar? Frustrated with machine learning books and courses?How do you get started in machine learning?Photo by Peter Alfred Hess, some rights reserved The most common question I’m asked by developers on my newsletter is: […]

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Machine Learning

8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset

Last Updated on August 15, 2020 Has this happened to you? You are working on your dataset. You create a classification model and get 90% accuracy immediately. “Fantastic” you think. You dive a little deeper and discover that 90% of the data belongs to one class. Damn! This is an example of an imbalanced dataset and the frustrating results it can cause. In this post you will discover the tactics that you can use to deliver great results on machine […]

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Machine Learning

Practice Machine Learning with Datasets from the UCI Machine Learning Repository

Last Updated on July 5, 2019 Where can you get good datasets to practice machine learning? Datasets that are real-world so that they are interesting and relevant, although small enough for you to review in Excel and work through on your desktop. In this post you will discover a database of high-quality, real-world, and well understood machine learning datasets that you can use to practice applied machine learning. This database is called the UCI machine learning repository and you can use […]

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Machine Learning

5 Ways To Understand Machine Learning Algorithms (without math)

Last Updated on August 12, 2019 Where does theory fit into a top-down approach to studying machine learning? In the traditional approach to teaching machine learning, theory comes first requiring an extensive background in mathematics to be able to understand it. In my approach to teaching machine learning, I start with teaching you how to work problems end-to-end and deliver results. So where does the theory fit? In this post you will discover what we really mean when we talk about […]

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