Build From Scratch ! Consists of models build in Python as well as Octave.
Most of the Machine Learning Libraries can be implemented in different Programming Languages using different open source libraries available online. Using one of those libraries, I have implemented the models in scikit-learn as well as from scratch in Octave. Programming in Octave was particularly tough, since there were no libraries used. But it was good Learning Experience :)
Whereas, the Visualization section was the most interesting one, you can find the different types of graphs and plots created by using libraries such as pandas, seaborn, matplotlib and plotly. One can find the code in the PythonBootCamp/Visualizations Folder and the Results are documented in the Wiki.
The PythonBootCamp folder has code files for all the topics including the Basic python Understanding to implementation of scikit high-fi ML libraries. All the files under the folder have '.pynb' extension and hence can be easily accessed in Jupyter Notebook. More information and Results in the Wiki Section