Notes on Codecademy Intermediate Python Courses

I thought Codecademy’s course “Getting Started Off Platform for Data Science” really deserved more focus than it did when I initially browsed the catalog, regretting that I saw it at the end of my perusal of beginner friendly Python courses. But life moves on. I started going through some intermediate courses with an eye on future studies in machine learning. Here are some notes:

  • Learn Recursion with Python I took purely for fun and curiosity with no expectation of applicability to modern machine learning. In school I learned recursion with Lisp, a language ideally suited for the task. Python wasn’t as good of a fit for the subject, but it was alright. Lisp was also the darling of artificial intelligence research for a while, but I guess the focus has since evolved.
  • Learn Data Visualization with Python gave me more depth on two popular Python graphing libraries: Matplotlib and Seaborn. These are both libraries with lots of functionality so “more depth” is still only a brief overview. Still, I anticipate skills here to be useful in the future and not just in machine learning adventures.
  • Learn Statistics with NumPy was expected to be a direct follow-up to the beginner-friendly Statistics with Python course, but it was not a direct sequel and there’s more overlap than I thought there’d be. This course is shorter, with less coverage on statistics but more about NumPy. After taking the course I think I had parsed the course title as “(Learn Statistics) with NumPy” but I think it’s more accurate to think of it as “Learn (Statistics with NumPy)”
  • Linear Regression in Python is a small but important step up the foothills on the way to climbing the mountain of machine learning. Finding the best line to fit a set of data teaches important concepts like loss functions. And doing it on a 2D plot of points gives us an intuitive grasp of what the process looks like before we start adding variables and increasing the number of dimensions involved. Many concepts are described and we get exercises using the scikit-learn library which implements those algorithms.
  • Learn the Basics of Machine Learning was the obvious follow-up, diving deeper into machine learning fundamentals. All of my old friends are here: Pandas, NumPy, scikit-learn, and more. It’s a huge party of Python libraries! I see this course as a survey of major themes in machine learning, of which neural networks was only a part. It describes a broader context which I believe is a good thing to have in the back of my head. I hope it helps me avoid the trap of trying to use neural nets to solve everything a.k.a. “When I get a shiny new hammer everything looks like a nail”.

Several months after I started reorienting myself with Python 3, I felt like I had the foundation I needed to start digging into the current state of the art of deep learning research. I have no illusions about being able to contribute anything, I’m just trying to learn enough to apply what I can read in papers. My next step is to learn to build a deep learning model.

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