Helper function scripts, cluster quality assessment, and PCA elaboration

[qualiaMachine]

Several additions here that are intertwined due to the addition of some helper functions. In case it is helpful, these changes can also be reviewed from my forked repo's website.

1. Added two helper scripts to reduce some of the more tedious coding (e.g., plotting code, coding least squares from scratch, etc.). These scripts are found in the code folder: clustering_helper_functions.py and regression_helper_functions.py. Functions from these files are imported throughout the regression, sklearn, and clustering episodes.

2. Edited setup file to link to helper functions

3. Regression episode a. Reference/use helper functions b. Added an exercise that clarifies parameters vs hyperparameters c. Added an exercise that reinforces the concept that models will fit their training data

4. Sklearn episode a. Reference helper functions b. Added some text explaining polynomial features

5. Clustering episode a. Reference helper functions b. Add cluster quality assessment section

6. Dimensionality reduction episode a. Added some code/text to demonstrate how PCA compresses data (% of total variance plots) b. Added a section that explores the efficacy of PCA when fitting data to a decision tree. This section probably only makes sense to cover if classification and decision trees are covered beforehand. I didn't have time to add a classification episode, but it's on my to-do list!