Learn to implement 10 Machine Learning algorithms from scratch with just Python and NumPy. A library hides the implementation details and if you're really looking to understand what goes behind the covers and understand how things work, this course has you covered.

This is a course by AssemblyAI where you don't rely on libraries like Pytorch or Tensorflow to implement the Machine learning algorithms but you implement them yourself from scratch with nothing but Python and NumPy.

Those algorithms that are going to be implemented are:

• K-Nearest Neighbors
  
  
• Linear Regression
  
  
• Logistic Regression
  
  
• Decision Trees
  
  
• Random Forest
  
  
• Naive Bayes
  
  
• PCA
  
  
• Perceptron
  
  
• SVM
  
  
• K-Means

You need basic Python, object oriented programming and the basics of NumPy to follow along as it's a practical course with a lot of code.

However scary math formulas are referred too.If you do have experience with Andrew Ng's deep learning courses which require high school level math and teach the basics of the notations  you should not face any issues even on that part.

All in all, it's a very interesting course bearing a fresh look at Machine Learning from an other perspective.All the accompanying code can be found on the course's Github repo.

More Information

Youtube playlist

Github

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