namas191297/bee_image_data_preprocessing

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Created about 5 years ago
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namas191297/bee_image_data_preprocessing

namas191297

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Pre-processing image data and files and preparing them to train a ML model

twitter_sentiment_analysis

Flask application used to analyze tweets and their sentiments based on a hashtag.

instaknow_backend

Analyse sentiments of Instagram users based on their post captions

medical_cost_estimator_startup

Fully deployed startup project to estimate/predict medical costs

artgan

mouse_gesture_control

Controlling the mouse using hand gestures

pneumonia_detection_cnn

Using transfer learning to detect Pneumonia from chest x-ray images

Jupyter Notebook

Machine-Learning-LinearRegression

Implementing a real life problem of calculating the fuel efficiency or a car with a dataset containing the variables. Implementation of Linear Regression in order to predict the fuel that may be used for 1200 miles with a calculation of R^2 and predicted value.

boston_crime_analysis

Exploratory data analysis and visualization of crimes in Boston

Jupyter Notebook

turkey_earthquake_analysis

Visual and exploratory data analysis of turkey earthquake data

Jupyter Notebook

compressive_sensing

Demonstration of compressive sensing (using l0 and l1 norm) on test data.

dogcat_breed_classifier

Classifying dog and cat breeds using transfer learning with ResNet34

ddpm

Implementation of a simple Denoising Diffusion Probabilistic Model (DDPM)

predicting_creditcard_approvals

Predicting credit card approvals using Machine Learning

Jupyter Notebook

amazon_product_webscraping

Extracting average price for any amazon product through web scraping using scrapy

bi_partitioning

k_way_partitioning

intraretinal_cystoid_fluid_detection_unet

Implementation of UNet to detect intraretinal cystoid fluid.

bee_classification

Classifying bees into Apis and Bombus using Deep learning

Jupyter Notebook

medicalcost_random_forest

Estimating medical costs using Random Forest regression

Jupyter Notebook

pattysitaliankitchen_menu_design

Menu and banner designs for a food startup

Machine-Learning-LARSPath

Implementing LARS with LASSO to visualize the regularization path by optimal selection of variables.

Machine-Learning-ElasticNetRegression

An implementation of the Elastic Net regression method to compare the difference between different regression methods.

fashion_mnist_gan

Generative adversarial network trained on the Fashion MNIST dataset

msft_stock_prediction

Comparing the accuracy of different regression models to predict MSFT stock price

Jupyter Notebook

gaming_logo_designs

Logos designed for different gaming clans/groups

mnist_digit_classifier

Neural Network trained on MNIST Database

Pygame

Small-scale Indie games developed in Python using pygame and related modules.

tedx_itmu_promotion_design

Brochures and poster made for a TEDx ITMU event using Adobe Photoshop.

rest_api_flask

Boilerplate code to deploy a REST API using Flask

Machine-Learning-RidgeRegression

Implementing a Ridge Regression model to generate data and predict the values.

sign_language_mnist_cnn

CNN trained to predict American sign language using sign language MNIST dataset

Jupyter Notebook

Machine-Learning-LassoRegression

Solving a problem using Lasso Regression to show the difference between Ridge-Regression and Lasso.

lr_fraud_detection

Fraud transaction detection from credit card data using logistic regression

Jupyter Notebook

weed_discriminant_nn

2 layer neural network to detect weeds in soybean crops

Jupyter Notebook

waste_classifier

Waste classification using deep learning

mobilenet_tfjs_week1

In-browser Image Classification using TensorFlow.js with MobileNet

tf_lowlevel_implementation

Using the low level tensorflow API to implement ML models

Jupyter Notebook

medical_cost_eda

Exploratory data analysis and visualization of medical costs based on several features

Jupyter Notebook

evaluating_mvs_in_cpc

Multi View Stereo on Internet Images

Machine-Learning-LogisticRegression

Implementing a LogisticRegression model to solve a categorical problem involving the record for a person with age and salary where we need to predict whether he/she will buy a laptop or not.

Machine-Learning-RidgeRegressionCV

Implementing a Ridge regression model using Cross-validation technique.

gaussianmixturemodels

Gaussian Mixture Models and Expectation Maximization with an example

Jupyter Notebook