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Today I started the bonus material of the intro to ML course I’ve been doing. I’ll be working with weather data will practise all the key topics I covered in the course. Key areas include: 1. Linear Regression – completed. 2. KNN – to do 3. Decision Trees – to do
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I completed the Machine Learning with Python introductory course today and received a fun digital certificate for it here. The course is designed as a six-week, part-time program, but I completed it in less than half the time by dedicating a little time every day. I’m also keen to
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K-Means Clustering Continued… I went through a lab exercise today on k-means. I’ve put the code on my Github page here. I remain in awe that you can fit a model with a few lines of code. A snippet is provided below. In this example, a k-means algorithm is
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K-Means Clustering. Today I learnt about k-means clustering. This is another one of those fancy-sounding machine learning techniques that seems intimidating to the uninitiated. But actually, the intuition behind it is simple. Thanks to Youtube, you can understand the rudimental elements of this unsupervised machine learning algorithm fairly quickly. Here’
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More on data. I read a bit more about the data problem in AI after writing about it yesterday. People smarter than I am believe there are diminishing returns to training AI models and ever growing datasets. In this research publication, AI researchers share the following summary: In layman terms
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On Data. One thing that’s clear as I work through this 100 day challenge is how important data is for training AI. We need lots of it, and apparently all the world’s data that’s available on the internet might not be enough for building more advanced models.
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Support Vector Machine. This is another intimidating machine learning term (along with terms like gradient descent!) However, you can use this concept in practice without digging into the crazy maths best left to the academics. Today, I completed a lab that runs you through a simple implementation of a support
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Logistic regression continued. In the lab portion of the intro to ML course today, I went through an exercise of running a logistic regression analysis on fictional customer data. I’ve put the code on Github here. The model is structured as follows: logit(p) = -0.2675 + (-0.1526 * tenure)
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Logistic Regression. On day 1 I worked with linear regressions — a statistical technique to predict a continuous value based on another variable or set of variables. In the last few days I’ve been wrestling with logistic regression. This is is a statistical technique that classifies data into a category.
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Python is the most popular machine learning programming language and across all general domains, it is the second most popular, behind Javascript. I’m probably at a intermediate skill level (but certainly on the lower end of that bracket) and I’ve decided to brush up on it regularly. To
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No coding today, but I did enjoy watching this video with computer scientists explaining machine learning in 5 levels of difficulty. One line in particular struck a chord with me: “We can now do in 5 lines of code, something that would have taken 500 lines of very mathematical messy
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Decision Trees. I went through the intro to ML material on decision trees this morning. I created some data in Excel and a simple decision tree machine learning model that predicts whether someone works in tech (value 1) or not (value 0). The data is synthetic, and I shaped it