Machine-Learning-Implementations

Implemented popular Machine Learning techniques and Algorithms

Contents

Linear Regression (using Gradient Descent)

• Using 2D dataset
• using Boston Dataset (in iPython file)

Logistic Regression

• Without Regularization along with Gradient Descent
• With Regularization using Advanced Optimization from Scipy (in iPython notebook)

Multiclass Classification

• Classifying Digits [0-9] using pictures as datasets (using Logistic Regression with Regularization)

Digit Classification using Neural Network

• Classifying digits using a single hidden layer Neural Network made from Scratch

Support Vector Machine (SVM)

• Implemented on 4 different datasets
• Includes linear as well as RBF (Gaussian) kernel
• Consists of a spam classifier too on preprocessed data with 1899 words in dictionary (from Coursera ML course)
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K-Means Clustering Algorithm

• Implemented on clusting 2D data in 3 sets and Image Compression

Principle Component Analysis (PCA)

• Implemented on 2D data and faces of people

Anomaly Detector

• Implemented Anomaly Detection on a 2D dataset

Recommender System

• Implemented Recommender System along with Collaborative Filtering for a set of Movies