I have gone on from Andrew Ng's class on Machine Learning to applying the concepts using scikit-learn and wrangling data using Pandas through Data School's tutorial

Machine Learning with scikit-learn with Data School

This is a free course offered by Data School.


The course is good for people who have completed Andrew Ng’s class on Machine Learning. You would start to realise how scikit-learn automates a lot of the low-level Octave code you implemented in Andrew Ng’s class from the simple gradient descent algorithm, to regularization, and the mind-boggling neural networks. Although I will be writing more on Neural Networks using Tensorflow (formerly known as Skflow).

Also, the tutorials offered by Data School are good for people who are new to scikit-learn and Python in general. If you do not want to watch his free open-source videos, you can check out my notes on all his tutorials.

  1. Introduction to Machine Learning
  2. IPython Introduction
  3. Exploring Iris Dataset
  4. KNN Classification Model
  5. Linear Regression Model
  6. Cross-validation for Parameter Tuning, Model Selection and Feature Selection
  7. Efficiently Searching Optimal Tuning Parameters
  8. Evaluating a Classification Model

I hope you found my notes useful! They are some variations on Github by others so feel free to use them too if you feel their explanations are clearer.

I will be moving on Pandas Series by Data School. Pandas plays an important role in data wrangling. Check out my review on this series soon!