Balaji S's Projects
A quick Q/A type document to understand about bias and variance tradeoff
Example to show how can we blend two images of same size and overlay a small image on top of a large image using OpenCV
Kaggle URL: https://www.kaggle.com/arpitjain007/dog-vs-cat-fastai
Weather updating Chatbot using RASA NLU
Dataset and Problem Statement: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image. The approach we are going to use here relies on deep learning. We will utilize a Convolutional Neural Network capable of colorizing black and white images with results that can even βfoolβ humans!
A bank is investigating a very high rate of customer leaving the bank. Here is a 10,000 records dataset to investigate and predict which of the customers are more likely to leave the bank soon.
Easy and Simple Visualizations
Detecting face masks using Python, Keras, OpenCV on real video streams
Using interquartile range IQR
Simple flames game with Python
Gender Detection using Python Keras and OpenCV on live camera
Basic image operations and drawing shapes using OpenCV
Functional and Object Oriented Plotting in Matplotlib
Contains the algorithms explained in my YouTube channel
Created a model to identify Spam messages and deployed the same using Flask. My website URL : https://model-deployment-portfolio.herokuapp.com/
Kaggle URL: https://www.kaggle.com/prasunroy/natural-images
This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine. In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming serviceβs number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset. Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.
Spacy Basics, Spans, Tokenization, Tokenization Visualizers, Stemming (Porter Stemmer and Snowball Stemmer), Lemmatization and Stopwords.