Free Python Project Coding Course
Learn Python by doing Python. Check out this free project-based course to quickly learn how to program in the high-demand language.
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They say the best way to learn is by doing, that practice leads to proficiency (which, in turn, leads to mastery), and that trial and error is key to success. If that's the case, learning Python by following a project-based approach makes good sense. Following that logic, you should probably check out this free Python project-based coding course from feeCodeCamp.
Put together by Kylie Ying, 12 Beginner Python Projects lives up to its name and clocks in at almost exactly 3 hours. It walks you from Python newbie to what you might call a proficient intermediate Pythonista in this length of time, all while focusing on the bigger picture. While many newcomers to Python — or, indeed, programming — start with by learning syntax and concentrating on understanding concepts in isolation, project-based learning forces one to think about where these concepts with into the bigger picture from square one.
It's one thing to learn about flow control on its own; it's something different to learn about how to practically implement it into a project that does much more.
What projects will you be tackling? in order:
- Madlibs
- Guess the Number (computer)
- Guess the Number (user)
- Rock Paper Scissors
- Hangman
- Tic-Tac-Toe
- Tic-Tac-Toe AI
- Binary Search
- Minesweeper
- Sudoku Solver
- Photo Manipulation in Python
- Markov Chain Text Composer
Kylie is a very solid explainer and takes a methodical approach to programming. If you have been looking for a project-based course to get your Python skills up to par, give this one a shot.
Don't forget to check out some other Python courses we have recently highlighted, including this Free Python Crash Course and this Free Python Automation Course.
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.