Aditya Kumar Gupta's repositories
Fraud-Detection
A Person Of Interest identifier based on ENRON CORPUS data.
Deep-Dream
A computer vision program which uses a convolutional neural network to find and enhance patterns in images, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images.
Facial-Recognition-with-PCA
Face Recognition Implementation using PCA, eigenfaces, and SVM
Feature-Scaling
A package that can transform features by scaling each feature into a given range. This is more lightweight and easy to use than sklearn.preprocessing.MinMaxScaler
Handwritten-Digit-MLP-Classification
Using Multi Layer Perceptron to build the model. Classifies the handwritten digits of the MNIST database with around 98% accuracy.
Lasso-Ridge-Regression-and-Elastic_Net-Regularization-from-Scratch
Basic implementation of Lasso, Ridge Regression and Elastic-Net Regularization.
Titanic-Survival-Exploration
Very basic data exploration of the Titanic Dataset.
AdaBoost-from-Scratch
A basic implementation of AdaBoost algorithm from Scratch.
Boston-Housing
Evaluating the performance and predictive power of a model. Cross questioned several concepts of ML for better understanding.
Classifying-Fashion-Clothes
Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28, gray-scale image, associated with a label from 10 classes.
Random-Forest-from-Scratch
A basic implementation of the Random Forest Classifier from Scratch and using Seaborn to find important features.
Text-Learning
Basic usage of NLTK. Implementation of concepts like Stemmer, TfIdf, and text.CountVectors
Time-Series-Prediction-from-Scratch
Training a simple RNN to do time-series prediction. Given some set of input data, it will be able to generate a prediction for the next time step.
Canny-Edge-Detection
A basic program that performs edge detection of images in real-time.
Decision-Tree-from-Scratch
A basic project to implement and visualize Decision Tree Classifier from Scratch.
deep-learning-v2-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
Donors-for-Charity
Employing several supervised algorithms to accurately model individuals' income.
EDA-on-Haberman-Dataset
The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago’s Billings Hospital on the survival of patients who had undergone surgery for cancer.
Feature_Format
This module can take a list of feature names and the data dictionary, and return a numpy array.
Handwritten-Digit-Prediction
Using the MNIST dataset which consists of greyscale handwritten digits. Each image is 28x28 pixels. Building and training a neural network that can take one of these images and predict the digit in the image.
KNN-from-Scratch
A basic project to implement the KNN classifier from Scratch.
ml-study-plan
The Ultimate FREE Machine Learning Study Plan
Naive-Bayes-Classification-from-Scratch
A basic project to implement Gaussian Naive Bayes.
SVM-Classification-from-Scratch
A basic project to build the classification model with SVM (Support Vector Machine)
Text-to-Speech.
A basic snippet of code that uses Google's gTTS API to convert text to speech. Go on. Play Around with it.