isl-notebooks

This repository contains R and Python notebooks code for An Introduction to Statistical Learning with Applications in R.

It's a learning playground for the book exercises including, merging and tweaking the codes from other repositories (see Credits) in the form of dockerized Jupyter notebooks so it is easier to quickly run and experiment with.

Notebooks

2. Statistical Learning
   2.3 Lab - Introduction to [R][[Python]
   2.4 Exercises - [R]
3. Linear Regresion
   3.6 Lab - Linear Regression [R][Python]
   3.7 Exercises - [R]

Running Jupyter

With Docker you can quickly setup Jupyter environment to run the notebooks and do your own explorations.
For more details see opinionated stacks of ready-to-run Jupyter applications in Docker.

To run the isl-notebook container run the following command:

docker-compose up --build

Wait for:

...
isl-notebook    |     Copy/paste this URL into your browser when you connect for the first time,
isl-notebook    |     to login with a token:
isl-notebook    |         http://localhost:8888/?token=your_token

to be displayed on your console and follow the instruction.

If you need to run some additional commands in the container run:

bash -c clear && docker exec -it isl-notebook sh

References

• G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer Science+Business Media, 2013
• T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Second Edition, Springer Science+Business Media, 2009

Resoures

• Statistical Learning by Stanford University
• In-depth introduction to machine learning in 15 hours of expert videos by Data School
• Python for Data Science and Machine Learning Bootcamp
• Data Science and Machine Learning Bootcamp with R

Credits

• IntroStatLearning
• ISL-python
• ISLR-python
• ISLR-with-Python
• stat-learning