Machine Learning Algorithms

Introduction

This repositry contains notebook files which includes fundamentals of data processing, how to handle data using numpy and pandas, how to visualize data using matplotlib and seaborn, and a basic intro into image classification by building a simple CNN model.

Algorithms

It also has a collection of notebooks of machine learning algorithms classified into supervised and unsupervised learning.

Supervised Learning

Linear Regression
Logistic Regression
Decision Tree
Naive Bayes
K Nearest Neighbors(KNN)

Unsupervised Learning

K Means Clustering
Principal Component Analysis(PCA)

The above algorithms are implemented using built in library functions. Additionally it also includes basic regression and classification tasks using Neural Networks(NN) and Multi Layer Perceptron(MLP).

Applications

Multinomial Naive Bayes classifier is used for Language Detection.

LSTM(RNN - Recurrent Neural Network) used for Microsoft Stock Price Prediction.