Sentdex Machine Learning with Python Review

Straight up, I really enjoyed Sentdex Machine Learning with Python course (by Harrison @Sentdex). You can find the course on Youtube, and on his website. I found it at a great introduction, especially the overall understanding and intuition.

Sentdex Machine Learning with Python

Sentdex Machine Learning with Python

The course starts with an introduction to regression, best fit slopes and then starts to move from one topic to the next such as KNN, SVM and then onto Deep Learning topics with Tensorflow. The difference I feel with this course is how deep Sentdex gets into the subject. Plus, I love his passion when he’s instructing.

In terms of depth, I think a good example is when he is teaching us KNN.

After showing us how to do it the ‘easier’ way (Scikit-Learn), Sentdex starts from scratch and helps us build an understanding of how it all works. I think this really helps in realising what you are actually doing. I also really enjoyed the use of deep learning with Kaggle data such as Cats vs. Dogs and his kernal on Kaggle for Lung Cancer Detection.

Favourite Parts:

  1. In depth analysis of some of the technics, such as coding linear regression and KNN from scratch. I think this helps in building an understanding of what is happening. There are times when he will go through the process without code as well, but in the end I think the code speaks more.
  2. Enthusiasm and really passionate when he explains the process.
  3. He makes mistakes, but shows them and corrects them in the end. In some cases he will insert himself in the future to correct a big mistake just so we don’t go along with it. I prefer seeing these mistakes personally when I’m learning. Although ‘some’ of the code is deprecated – or very soon to be, you can always look at the Youtube comments and someone would have corrected it!
  4. Quick tip – you probably need to know a bit about Python to follow through. I recommend a simple introduction course such as Udacity, Udemy or Edx (MIT).
  5. My GITHUB (just going through sections).