From 529817acf3851706fd6ce1d2773db2abb0811a71 Mon Sep 17 00:00:00 2001
From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com>
Date: Wed, 13 Dec 2023 21:44:42 +0000
Subject: [PATCH] feat(index.md): add links to resources

---
 docs/en/index.md | 34 +++++++++++++++++++++++++++++++---
 1 file changed, 31 insertions(+), 3 deletions(-)

diff --git a/docs/en/index.md b/docs/en/index.md
index 6a2b8c87e..5f2ef5e74 100644
--- a/docs/en/index.md
+++ b/docs/en/index.md
@@ -16,14 +16,20 @@ The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-serv
 
 **[👉 Click here to set up your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**
 
+### Videos
+
+- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech, a series dedicated to helping you get started with Kubeflow! Learn more about what Kubeflow is, the different parts of Kubeflow, and how to get started with simple, portable, and scalable machine learning.
+
+### Documentation
+
+- [Official](https://www.kubeflow.org/)
+- [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf)
+- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf)
 The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks:
 
 - Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage).
 - Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees.
     
-
-
-
 ## Getting Started
 
 ### AAW Portal
@@ -64,6 +70,28 @@ List of sources of examples:
 1. one
 2. two
 
+## Learning Resources
+
+- https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/
+
+### Data Preparation
+
+- https://machinelearningmastery.com/start-here/#dataprep
+
+
+### Machine Learning 
+
+- https://machinelearningmastery.com/start-here/#imbalanced
+
+### Python
+
+- https://machinelearningmastery.com/start-here/#pythonskills
+- https://www.youtube.com/watch?v=t8pPdKYpowI
+
+### R
+
+- https://www.youtube.com/playlist?list=PLLOxZwkBK52C6_Nkmp0nFCreLfnfJgUL7
+
 ## Need Help?
 
 Join our vibrant community on the [Slack channel](https://statcan-aaw.slack.com/) to connect with fellow users, ask questions, and share experiences.