AmalVijayan

BERT-For-Multi-Class-Classification

Time-Series-Forecasting---Predicting-Bitcoin-Prices

Time Series Forecasting: Predicting Bitcoin Price The cryptocurrency market has seen its rise and fall in the past few years. With a variety of coins being exchanged for real money, it is important to know the trend in the coin price. In this article, we will build a fairly simple LSTM network to predict or forecast the prices of Bitcoin. Obtaining Bitcoin Data There are plenty of open sources available on the internet to extract historical data of Bitcoin prices. The one that I have used below is from https://coinmarketcap.com/.

Image-Classififcation-Hackathon

Solution for Intel Scene Classification with an attained accuracy of 73% on the test set.

Image-Classification-MNIST

The code is given in a Jupyter Note (.ipnb) file with all definitions and description.

bobseye

Classifying-Movie-Scripts

DataScience

Data Science Learning and Practice

SpeechClassifier

Text Classification is one of the most common problems in NLP. In the past few years, there have been numerous successful attempts which gave rise to many state-of-the-art language models that is capable of performing the classification task with mind boggling accuracy and precision. Text classification powers many real world applications from simple spam filtering to voice assistants like Alexa. These applications have the capability to classify the user’s input to understand the context of spoken words. In this article, we will build on the basic idea of giving the machine the power to listen to human speech and classify what the person is talking about. In one of our previous tutorials we talked about a library called Simple Transformers that makes it effortless to implement many transformer based state-of-the-art language models. We used the library to classify any number of input texts into one of the following four news sections Politics Technology Entertainment Business We will use the same classifier to classify what you speak into one of those four categories effortlessly with very minor changes in code.

AmazonReviewScraper

This python code can be used to scrap reviews of a specific product from Amazon,

AmalVijayan/bobseye

AmalVijayan/SpeechClassifier

Text Classification is one of the most common problems in NLP. In the past few years, there have been numerous successful attempts which gave rise to many state-of-the-art language models that is capable of performing the classification task with mind boggling accuracy and precision. Text classification powers many real world applications from simple spam filtering to voice assistants like Alexa. These applications have the capability to classify the user’s input to understand the context of spoken words. In this article, we will build on the basic idea of giving the machine the power to listen to human speech and classify what the person is talking about. In one of our previous tutorials we talked about a library called Simple Transformers that makes it effortless to implement many transformer based state-of-the-art language models. We used the library to classify any number of input texts into one of the following four news sections Politics Technology Entertainment Business We will use the same classifier to classify what you speak into one of those four categories effortlessly with very minor changes in code.

AmalVijayan/go-cli-tool

How to build a cli tool with Golang

AmalVijayan/aws-mfa-profile

Creates adds a temporary MFA profile with session token to aws configuration

AmalVijayan/Scheduler

AmalVijayan/AudioMetaServer

AmalVijayan/avn_pro_portfolio

A professional looking and highly responsive Portfolio App using ReactJs

AmalVijayan/React-YouTube

A functional clone of YouTube using ReactJS

AmalVijayan/React-Spotify

Spotify clone using React.js :: https://youtu.be/pnkuI8KXW_8?list=LLJjKMgZdkL01X8Z6h-fWPPQ

AmalVijayan/github-actions-continuous-delivery

AmalVijayan/Classifying-Movie-Scripts

AmalVijayan/DeployingMLModel

Deploying a FastAi regression Model using Flask API

AmalVijayan/SimpleTransformers

In the past few years, we have seen tremendous improvements in the ability of machines to deal with Natural Language. We saw algorithms breaking the state-of-the-art one after the other on a variety of language-specific tasks, all thanks to transformers. In this article we will discuss and implement transformers in the simplest way possible using a library called Simple Transformers.

AmalVijayan/BERT-For-Multi-Class-Classification

AmalVijayan/Regression-With-FastAi

AmalVijayan/Predict-The-Price-Of-Books-By-MachineHack

Solution to Predict The Price Of Books Hackathon By MachineHack

AmalVijayan/Time-Series-Forecasting---Predicting-Bitcoin-Prices

Time Series Forecasting: Predicting Bitcoin Price The cryptocurrency market has seen its rise and fall in the past few years. With a variety of coins being exchanged for real money, it is important to know the trend in the coin price. In this article, we will build a fairly simple LSTM network to predict or forecast the prices of Bitcoin. Obtaining Bitcoin Data There are plenty of open sources available on the internet to extract historical data of Bitcoin prices. The one that I have used below is from https://coinmarketcap.com/.

AmalVijayan/BayesianOptimization-On-XGBoost

Using Bayesian Optimization to tune Hyper-parameters of XGBClassifier on MachineHack's Whose Line Is It Anyway: Identify The Author Hackathon

AmalVijayan/Matplotlib_Examples

AmalVijayan/GAN_With_Keras

Introduction To GAN

AmalVijayan/ObjectDetection_Inference

Object Detection Inference using pre-trained model -ssd_mobilenet_v1_coco_2017_11_17 with Tensorflow

AmalVijayan/NLP-Hackathon_Solution

Solution for Predict The News Category Hackathon by MachineHack

AmalVijayan/Introduction_To_Geographical_Plotting

Geographical Plotting with Plotly

AmalVijayan/Multilabel_Image_Classification

A simple Multi-Label Classification to classify Cats and Dogs into respective breeds.

AmalVijayan/Quick_Tips

Data exploration useful methods

AmalVijayan/Image-Classififcation-Hackathon

Solution for Intel Scene Classification with an attained accuracy of 73% on the test set.

AmalVijayan/Scrapy

All Scrapy Codes

AmalVijayan/DataScience

Data Science Learning and Practice

AmalVijayan/Clustering

Clustering in Python and R

AmalVijayan/Image-Classification-MNIST

The code is given in a Jupyter Note (.ipnb) file with all definitions and description.