metpallyv/Logistic_Regression

Stars
2
Language
Python
Created about 9 years ago
Updated over 8 years ago

metpallyv/Logistic_Regression

metpallyv

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Goal of this project is to implement Logistic Regression without using Machine Learning Libraries

MovieRecommendation

The goal of this project is to build a movie recommendation system using Non negative regularized matrix factorization without using any Machine Learning libraries on movie lens dataset

SVM-Kernels

Goal of this project is to implement perceptron,Dual perceptron,Linear Kernel and RBF kernel without using any Machine Learning Libraries

DecisionTrees

Goal of this project is to build Classification Decision Trees and Regression Decision trees without using any Machine learning libraries

ApacheSpark_MachineLearning_Scala

This Project is all about running Machine Learning algorithms on a distributed file system(HDFS) using Apache Spark and Scala

Clustering

Goal of this project is to implement K-means clustering and GMM clustering without any Machine Learning libraries

NaiveBased

Goal of this project is to implement Multivariate NaiveBased Classifier without using any Machine Learning libraries

SearchEngine

The goal of this project is to implement a simple Search Engine using various retrieval models using tf-idf and BM25

Regularized_Logistic_Regression

Goal of this project is to implement Regularized Logistic Regression without using any Machine learning libraries

TwitterAnalytics

The goal of this project is to be able to perform Twitter Analytics using the Twitter Rest APIs

LinearRegression

Goal of this project is to implement Linear Regression using Gradient Descent algorithm and Normal Equations without using any Machine learning libraries

NeuralNetwork

Goal of this project is implement BackPropogation Algorithm using Neural Networks

Polynomial_Regression

Goal of this project is to implement Polynomial Regression using Normal Equations and without using any Machine learning libraries