Learn machine learning algorithms from scratch with Python
Learn how to implement 10 machine learning algorithms from scratch with just Python and NumPy. A library hides implementation details and if you’re really looking to understand what’s behind the covers and understand how things work, this course has you covered.
This is an AssemblyAI course where you don’t rely on libraries like Pytorch or Tensorflow to implement the machine learning algorithms, but implement them yourself from scratch with nothing but Python and NumPy.
The algorithms that will be implemented are:
- K-Nearest Neighbors
- Linear regression
- Logistic regression
- Decision trees
- random forest
- naive bayes
You need basic Python, object-oriented programming, and basics of NumPy to take as this is a hands-on course with lots of code.
However, scary mathematical formulas are also mentioned. If you have experience with Andrew Ng’s deep learning courses that require high school level math and teach the basics of notations, you shouldn’t have any trouble even on this part.
All in all, it’s a very interesting course that takes a fresh look at Machine Learning from another angle. All the accompanying code is on the course’s Github repo.
Fast.ai’s hands-on deep learning for coders has been updated
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