A Python step-by-step primer for Machine Learning and Optimization.


General Machine Learning tutorials

A Python step-by-step primer for Machine Learning and Optimization

This github repository gathers python language training for Machine Learning and Optimization from basics of Python programming to Deep Learning.


Simple and step-by-step. One goal of early-ML is to show how to use some classical ML or data-related packages (such as sklearn) but also to have a deeper understanding of some ML algorithms where we use simple and plain Python to re-create our Machine Learning and Optimization routines.


Depending on your Python level, best is to start to have a look at the organisation of the repository and pick the subject you are interested in.


Start with cloning the repository

git clone https://github.com/dbetteb/early-ML.git

and then go to early-ML folder and jump on the subjects you want to get trained to.


You should have a Python 3.5+ installation working with the following packages

  • numpy, scipy
  • pandas
  • scikit-learn
  • ipython
  • jupyter
  • plotly

Why early-ML ?

There exists tons of training on Machine Learning with Python. However this ones focuses on early principles and explaination behind the scene. Many people figure they understand Gradient Boosted Machines for instance since they obtain good results with xgboost package for instance but they do not know about the machinery and the algorithms behind. early-ML will let you figure out about the algorithms !