Part 2: Training Machine Learning Algorithms

Project 4

In this part, we will use a Python library called SciKit Learn. This library has implementations for multiple machine learning algorithms. We will focus on three of them:

• K-Nearest Neighbors
• Linear Classifier
• Neural Network

We will use the SciKit Learn implementations for each of these. Your job will be to train the best versions of the K-Nearest Neighbors and Neural Network algorithms using Cross Validation. The Linear Classifier will be used as a baseline (your Neural Network must outperform it!).

Open the notebook Machine Learning with SciKit Learn.ipynb using the Google Drive link provided, and save a copy in your Drive. The text in the notebook guides you through each task. Places where you need to write code are noted in the comments, with the label TODO. Please read through the notebook carefully to make sure you do not miss any important instructions.

The end of this notebook contains a cell which allows you to aave your favorite classification algorithm to a file. You should run it, then download the file for Part 3. With your partner, select the best algorithm from both of your notebooks to use.