Deep Learning, NLP, NMT, AI, ML
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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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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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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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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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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Last Updated on August 12, 2019 Microsoft recently launched support for machine learning in their Azure cloud computing platform. Buried in some of their technical documentation for the platform are some resources that you may find useful for thinking about what machine learning algorithm to use in different situations. In this post we take a look at the Microsoft recommendations for machine learning algorithms and the lessons that we can use when working through machine learning problems on any platform. […]
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Last Updated on August 15, 2020 Implementing machine learning algorithms from scratch seems like a great way for a programmer to understand machine learning. And maybe it is. But there some downsides to this approach too. In this post you will discover some great resources that you can use to implement machine learning algorithms from scratch. You will also discover some of the limitations of this seemingly perfect approach. Kick-start your project with my new book Machine Learning Algorithms From Scratch, […]
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Last Updated on August 15, 2020 Get Started And Avoid Getting The Wrong Advice Machine learning is a fascinating and powerful field of study filled with algorithms and data. The thing is, there are so many different types of people interested in machine learning, and each has different needs. It is important to understand what it is you want from machine learning and to tailor your self-study to those needs. If you don’t, you could very easily go down the rabbit […]
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Last Updated on August 16, 2020 Programmers learn by implementing techniques from scratch. It is a type of learning that is perhaps slower than other types of learning, but fuller in that all of the micro decisions involved become intimate. The implementation is owned from head to tail. In this post we take a close look at Joel Grus popular book “Data Science from Scratch: First Principles with Python“. I recently finished reading the paperback version and I think it […]
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Last Updated on August 15, 2020 How do you get accurate results using machine learning on problem after problem? The difficulty is that each problem is unique, requiring different data sources, features, algorithms, algorithm configurations and on and on. The solution is to use a checklist that guarantees a good result every time. In this post you will discover a checklist that you can use to reliably get good results on your machine learning problems. Machine Learning ChecklistPhoto by Crispy, […]
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