100 Days Of ML

Table of Contents

1. Installation
2. Project Motivation
3. Topics
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

The project uses the following libraries in addition to Anaconda with python version:3.7.4.

• pandas
• numpy
• seaborn
• plotly
• sklearn
• xgboost

Project Motivation

In this project, we will implement traditional machine learning algorithms, deep learning algorithms and other statistical concept that are used in machine learning.

Topics

Day 1: Linear Regression, Lasso Regression and Ridge Regression with gradient Descent

Day 2: Logistic Regression

Day 3: Naive Bayes Classifier

Day 4: K-Nearest Neighbor

Day 5: Multi Layer Perceptron

Day 6: Support Vector Machine

Day 7: Decision Tree Classifier

Day 8: K-Means Clustering

Day 9: Principal Component Analysis

Day 10: Neural Network