Python Implementations of Machine Learning Models

Erik Liner Noren - Python implementations for machine learning algorithms

This post highlights some great pages where python implementations for different machine learning models can be found. If you are a data scientist who wants to get a fair idea of whats working underneath different machine learning algorithms, you may want to check out the Ml-from-scratch page. The top highlights of this repository are python implementations for the following:

Here is an insight into implementation of different types of regression algorithms. The code found on this regression page provides implementation for the following types of regression:

  • Linear regression (Least squares method for loss function)
  • Lasso regression (L1 norm regularization)
  • Ridge regression (L2 norm regularization)
  • Elastic net regression (regularized regression method that combines L1 and L2 penalties against model complexity)
  • Polynomial regression
  • Polynomial ridge regression  

Very helpful indeed! Thanks to Erik Liner-Noren for putting these pages. Enjoy learning and implementing machine learning.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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