R and Data Files from my YouTube Channel

Implementation of Machine Learning Algorithms (KNN, Linear, Logistic, SVM, K-Means, Decision Tree, Naive Bayes) from Scratch using Python & Numpy only

Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.

Applying different machine learning algorithms on PCGA Prostate Cancer Gene Dataset for Feature Selection, Dimensional Reduction and Classification and Regression

LDA(Linear Discriminant Analysis) for Seed Dataset

The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

Analysing different dimensionality reduction techniques and svm

Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance

Various Machine learning algorithms

Explore facial recognition through an advanced Python implementation featuring Linear Discriminant Analysis (LDA). This repository provides a comprehensive resource, including algorithmic steps, specific ROI code and thorough testing segments, offering professionals a robust framework for mastering and applying LDA in real-world scenarios.

Diabetes detection in patients using different machine learning techniques and comparing the algorithms based on confusion matrix and other metrics.

Analyze a dataset on muscular dystrophy and make statistical inferences

Using classification algorithms to predict the geographical origin of an individual.

Machine learning algorithms from scratch in python.

NUS Pattern Recognition module graded assignments

In this project we conducted linear discriminant analysis to determine whether a given car is above or below the median mpg.

Data Understanding using- PCA, LDA, tSNE, and UMAP.

Exploratoy Data Analysis,Logistic Regression,Penalized Logistic Regression (LASSO), LDA, Decision Trees, Bagging, Random Forest

Participating in Hacktoberfest 2022. Code performing dimensionality reduction on datasets accepted.