R and Data Files from my YouTube Channel

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/)

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

LDA(Linear Discriminant Analysis) for Seed Dataset

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

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

Analysing different dimensionality reduction techniques and svm

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.

Various Machine learning algorithms

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

Implementation of a sketch‐recognition pipeline inspired by Google’s Quick, Draw!. Includes data preprocessing and feature‐engineering scripts, three Bayesian classifiers alongside Logistic Regression, SVM, K-NN and XGBoost baselines, and an RNN model.

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

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

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

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

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

NUS Pattern Recognition module graded assignments

Analyze a dataset on muscular dystrophy and make statistical inferences