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Hi Reader,
In case you havenโt checked out the Data School blog in a while, Iโve published a few new posts:
These are expanded versions of past Tuesday Tips!
โSQL Tutorial for Data Scientists & Data Analysts (free)
Although Python dominated the โTop Programming Languages of 2023โ, SQL took first place when ranked by job postings ๐ฅ (source).
If youโre looking to learn SQL, the tutorial above includes 30+ lessons and 40+ practice problems you can try directly in the browser, some of which were sourced from real Data Science interviews!
When faced with a new classification problem, Machine Learning practitioners have a dizzying array of algorithms from which to choose: Naive Bayes, decision trees, Random Forests, XGBoost, neural networks, and many others.
Where should you start? For many practitioners (including myself), the first algorithm to reach for is one of the oldest in the field: Logistic regression.
Here are a few attributes of logistic regression that make it incredibly popular:
In other words, it helps you to get going quickly with your Machine Learning project! You can focus your energy on building your initial ML pipeline (from data ingestion to prediction) without spending much computational time or code on model training and tuning.
Although you can use a ML algorithm without truly understanding it, learning how it works will ultimately help you to develop an intuition for when to use it and how to tune it.
To gain that deeper understanding, I recommend reading this lesson from my Data Science course:
๐ Logistic regression lesson (Jupyter notebook)
During this lesson, youโll learn:
If you get stuck on any of the concepts in the lesson, the resources listed in my logistic regression guide will help you to get un-stuck!
If youโve decided to use logistic regression, youโll need to tune it in order to maximize its performance. Iโve got a short video that will teach you how to tune logistic regression in scikit-learn:
๐ Important tuning parameters for LogisticRegression (video)
For more details, check out the scikit-learn documentation.
If you enjoyed this weekโs tip, please forward it to a friend! Takes only a few seconds, and it really helps me grow the newsletter! ๐
See you next Tuesday!
- Kevin
P.S. I thought you said this was a linear systemโ
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Kevin Markham
Join 25,000+ aspiring Data Scientists and receive Python & Data Science tips every Tuesday!
Hi Reader, Last week, I recorded the FINAL 28 LESSONS ๐ for my upcoming course, Master Machine Learning with scikit-learn. That's why you didn't hear from me last week! ๐ I edited one of those 28 videos and posted it on YouTube. That video is today's tip, which I'll tell you about below! ๐ Tip #45: How to read the scikit-learn documentation In order to become truly proficient with scikit-learn, you need to be able to read the documentation. In this video lesson, Iโll walk you through the five...
Hi Reader, happy Tuesday! My recent tips have been rather lengthy, so I'm going to mix it up with some shorter tips (like today's). Let me know what you think! ๐ฌ ๐ Link of the week A stealth attack came close to compromising the world's computers (The Economist) If you haven't heard about the recent "xz Utils backdoor", it's an absolutely fascinating/terrifying story! In short, a hacker (or team of hackers) spent years gaining the trust of an open-source project by making helpful...
Hi Reader, Today's tip is drawn directly from my upcoming course, Master Machine Learning with scikit-learn. You can read the tip below or watch it as a video! If you're interested in receiving more free lessons from the course (which won't be included in Tuesday Tips), you can join the waitlist by clicking here: Yes, I want more free lessons! ๐ Tip #43: Should you discretize continuous features for Machine Learning? Let's say that you're working on a supervised Machine Learning problem, and...