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ECE_196_Intro_to_ML

This repository provides an introduction to Machine Learning through a series of demos and exercises, with lecture slides to accompany them. The structure of the repository is as follows:

/Resources 
  
  Machine_Learning.pptx -- Lecture slides for an introduction to what Machine Learning (ML) is, an overview of classical ML models, and steps of an ML project
  
  sklearn_flowchart.png -- A flowchart of sklearn models to choose from for any given dataset

/ML\ Demos -- folder of all demo files of ML-related projects and exercises
  
  data/* -- all data files needed for the demos
  
  NBA_Win_Classification.ipynb -- end to end ML project for classifying when an NBA team will win. Provides a baseline model after processing and producing features for collected data.
  
  Titanic\ ML\ Demo.ipynb -- Walks through steps of a ML project and the series of problems that can be assesed with the classic Titanic dataset for Machine Learning.
  
  kNN_MNIST.ipynb -- Provides an introduction to using sklearn to apply PCA to and classify handwritten digits on the famous MNIST dataset via the kNN algorithm.
  
  sklearn_practice.ipynb -- Introduces using sklearn and seaborn on Iris dataset to apply PCA, visualize the dataset, and predict classes with Logistic Regression.
  
  sklearn_practice.pdf -- a pdf version of sklearn_practice.ipynb to be used for reference when using sklearn.
  
/Web\ Scraping -- web-scraping exercises and examples
    
  Intro_to_Web_Scraping_exercise.ipynb -- 3 exercises introducing you to using BeautifulSoup on example.webscraping.com
    
  NBA_Data_Collection.ipynb -- example of scraping team data from stats.nba.com
  
  chromedriver.exe -- Executable for automated chrome browser (works for chrome version 87)

Answers to each of the files in ML_Demos/ can be found at https://drive.google.com/drive/folders/10OjXyX3CjFgVij88tKi64dGIQkbpZx9V?usp=sharing.
This folder will be updated throughout the quarter as we go over each demo.

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