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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
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Yesterday, I skimmed through k-fold cross-validation. I wanted to return to it today because I still wasn’t sure about what exactly we are assessing with it, and why. Here‘s the clearest Youtube video I found on the topic. The screenshot below from that video provides an easy summary.
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Today I learnt about cross validation (scikit-learn has Python helper functions for this.) This is where you split your training and testing data into different sets, and then you iteratively train and test against different combinations to assess how well a model performs on unseen data. Two common methods of
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Today, I went through a classifier lab on the intro ML course. There are several bits I didn’t quite understand but GPT helped get me over the basics. For example, I will need to review my notes on the Jaccard Index and F1-score (evaluation metrics for classifier models), and
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Today I continued with the IBM intro ML course, and got to build a multilinear regression model. This time I tried to do it with data outside of the class. I found some data on IMDB movies that also had a Metacritic score and a number of other features. Below
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I spent bits of today watching Youtube videos about linear regression. One of the best ones I found was this StatQuest video on the topic. I enjoyed his style so much, I ordered his introductory book on Machine Learning. I’m planning a digital fast over a long weekend in
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Today I’ve started a “100 days of AI” challenge. I’ve used LLMs across a few personal projects here, here, and here. But I’d like to understand the basics of AI and machine learning a bit better. My motive isn’t to retrain as a ML engineer or
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After the OpenAI developer event yesterday investor Harry Stebbings tweeted, “Holy shit. Can you imagine a GPT where you could ask any question and it uses advice from 3,000 20VC episodes to answer your questions from the best VCs in the world.” That possibility is in fact already a