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The topic of the day was regression trees. They are similar to classification trees but are used to predict continuous values. I only went through a revision primer and can’t say I’ve internalised the full concepts. However, I know the process at a very high level. Machine Learning
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Today I read through how to build a classification tree. There’s a great video on this here from StatQuest. I’m glad we have software that can do the work. Going through it by hand is a hell of a task, even when you have just half a dozen
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About 3 weeks ago (27 Apr) I started the Python for Data Science, AI & Development course. It’s far easier than the Machine Learning with Python course I completed on Day 17. I was able to complete the python course on the side alongside my usual AI readings and
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Today, I returned to decision trees. Even though I was building decision tree models by day 7 of this challenge, I could do with going back to the theory and understanding it better. And that’s what I started doing today. Before starting, we should know that there are two
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I’m about 60% through this workbook, and I have to admit, I really I’m skipping most of the more mathsy bits. I read the Ridge (L2) and Lasso (L1) Regularization sections today and though I didn’t follow the maths fully, at least I came away with the
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Today was a Python practice day. I spent about an hour working through exercises in this book. Python is the most popular language for machine learning and AI so it helps to know it well. It’s also a fantastic generalist language to prototype ideas quickly.
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This morning I made a start on learning about “regularization” in machine learning. When we train sophisticated machine models, sometimes they “overfit” the training data. In the example below from Andrew NG, you can see the third chart on the far right has a blue line (the model) that fits
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I continued working through some Numpy (numerical python) exercises today to better understand why the library is so useful. Turns out, it’s great at dealing with arrays and a variety of numerical computations. Running linear algebra, statistical operations, and other calculations with Numpy is faster and more memory efficient
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I worked through a fast primer on Numpy this morning. Numpy is an open-source Python library for mathematical functions. It’s used often in data science and machine learning so it’s worth knowing the basics. I also enjoyed reading this article which has a moderate view of what large
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What is AI, really? Lots of new tech startups have “AI” in their pitches these days. The media and press also use the words “AI” very broadly, and I’ve been guilty of it, too. “AI” is catch-all term for lots of technologies and techniques, and it isn’t always
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Yesterday I learnt about ROC curves. There’s an extension to this concept: the Area Under each Curve (AUC). If you have two different ROC curves from different models, finding the area under each curve will reveal which model performs better. Here’s an extract from the StatQuest book that
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Today, I went over ROC graphs. The acronym stands for receiver operating characteristic. This concept originates from signal detection theory during World War II. Radar operators wanted to ascertain how good they were at detecting enemy aircraft (“True Positives”) rather than confusing them for birds or some other noise (“False