machine learning :3

ML Devlog 3!
I just started the Scikit-Learn MOOC course to learn the basics of Scikit-Learn and machine learning as a whole! :D

Today, I completed the Tabular Data Exploration section of the course and visualized some historical 1994 census data.

While not recording, I also set up an Anaconda environment for my ML projects, and it includes libraries such as pandas, numpy, matplotlib, seaborn, and scikit-learn. I then loaded this environment into VS Code, where I was able to open a notebook file to code in.

For the 0 people wondering, I have switched (again) off of the Google course because my goal involves using Scikit-Learn, which the Google course would not teach. :(

Thanks for reading :3

Machine Learning Theory!
Hello everyone! As I mentioned previously, I switched courses from Kaggle to Google’s Machine Learning Courses!

Starting from the beginning, I did the Intro to ML foundational course, which was more about theory than making an ML model, but I’ll get there someday…

This was a very short sesh, so I don’t want to write more, but I hope that I can get into learning to make/train an ML model soon :)

Learning Pandas (for machine learning)!
I was going through the Kaggle Intro to Pandas Course (because in a sesh I forgot to record, it told me to finish that one first), and I got through the first two steps :D
Took me 51 minutes, but the course itself said it would take 4 hours, so I’m ahead of it!

HOWEVER, I probably won’t continue the Kaggle course since, at the end, I was getting ragebaited because their provided solutions were getting flagged as incorrect… >:(

Next time, I’ll probably go through Google’s ML courses, since I can’t tell if Kaggle is broken or if I have a major skill issue to the point where copying and pasting messes up.