Moiz Hussain's starred repositories
Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
awesome-algorithms
A curated list of awesome places to learn and/or practice algorithms.
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
nlp_course
YSDA course in Natural Language Processing
machine_learning_examples
A collection of machine learning examples and tutorials.
SDE-Interview-Questions
Most comprehensive list :clipboard: of tech interview questions :blue_book: of companies scraped from Geeksforgeeks, CareerCup and Glassdoor.
data-science-question-answer
A repo for data science related questions and answers
image-to-image-papers
🦓<->🦒 🌃<->🌆 A collection of image to image papers with code (constantly updating)
papers-I-read
A-Paper-A-Week
ml-interview
Preparing for machine learning interviews
nlp-tutorial
Tutorial: Natural Language Processing in Python
Bayesian-Spam-Filter
An implementation of a Spam Filter in Python that uses the Naive Bayes Model to classify mails as spam or ham.
dynamicTreeCut
Python translation of the hybrid dynamicTreeCut method as created by Peter Langfelder and Bin Zhang.
semeval2018-task7
Code for "GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification"
spam-filter
A spam-ham filter using NLTK Naive Bayes classifier on Enron spam corpus .
Viterbi-Algorithm
Viterbi Algorithm for POS tagging of sentences using Brown corpus
NLP-Language-Model
A language model on gutenberg corpus which allows speel check , word completion and gramatical correction
IMDB-dataset-exploration-and-analysis
The objective of the project is to identify the various predictors and characteristics that help in the prediction of IMDB ratings for a particular movie. The project includes exploratory analysis on the dataset to derive meaningful interpretation between the predictors and outcome. We built models to find out which predictors will help in deciding the rating. Further text mining has been done on the titles of the movies given in the dataset to derive valuable information about the popularity and rating of the movies