This repository contains slides and code for the Spring session of MDaS boot camp on Machine Learning.
The boot camp is divided into two 1.5-hour sessions and has been structured to introduce Python usage for Machine Learning using the scikit-learn library. By the end of these sessions, students will be able to perform data pre-processing and implement simple ML models from sklearn and analyze their results.
The following models will be discussed during the sessions:
- Decision Trees
- Naive Bayes
- Logistic Regression
- Support Vector Machine
- Neural networks
The goal is to build these models in Python and report prediction statistics on the test data. Task 1 includes DTs and NB and Task 2 includes the rest of the models.
Please refer to the Fall boot camp for a brief introduction to Python programming (numpy, pandas, matplotlib) and instructions for installation. The code is provided in Python notebook format and can be run via Jupyter Notebook or Google colab
Install Python packages via pip
pip install numpy pandas matplotlib scikit-learn
Download the materials in this repository. If you're familiar with git, you can clone this repository by running the following command in the terminal (git bash)
git clone https://github.com/karuna-bhaila/mdas_ML.git