Machine Learning: A Quick Introduction

Sanjiv R. Das

Jupyter Extensions

These allow using the notebooks in myriad ways, see: https://blog.jupyter.org/99-ways-to-extend-the-jupyter-ecosystem-11e5dab7c54

%pylab inline
import pandas as pd
import os
%load_ext rpy2.ipython

Populating the interactive namespace from numpy and matplotlib

%%capture
!pip install ipypublish
from ipypublish import nb_setup

# Basic lines of code needed to import a data file with permissions from Google Drive
from google.colab import drive
# drive.mount("/content/drive", force_remount=True)
drive.mount('/content/drive')
os.chdir("drive/My Drive/Teaching/OnlineMSFA_FNCE2431/FNCE2431_Machine Learning for Finance/3_Course Content/Notebooks/")

Mounted at /content/drive

nb_setup.images_hconcat(["DSTMAA_images/ML_AI.png"], width=700)

https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12

5 Applications of ML in Finance

https://www.verypossible.com/blog/5-applications-of-machine-learning-in-finance

https://towardsdatascience.com/machine-learning-in-finance-why-what-how-d524a2357b56

nb_setup.images_hconcat(["DSTMAA_images/ML_use_cases.jpg"], width=600)

nb_setup.images_hconcat(["DSTMAA_images/AI_solutions.jpg"], width=600)