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2020_leetcode-company-wise-questions

It has PDF of questions of leetcode according to company wise.

6companies30days

#ReviseWithArsh: https://www.youtube.com/watch?v=QUnaBYKQkZU

6companies30days-1

Challenge to solve 90 questions from 6 companies in 30 days. Solved 90/90.

all-about-ai-residency

AI residency programs information

anwesha

The work presents “Anwesha: A tool for Semantic Search in Bangla”. This work shows explorations toward building a search engine prototype in Bangla language. The work used sources from WordNet, Wikipedia and statistical co-occurences(LSA) for retrieval of semantically related documents.

anwesha_lemmatiser

anwesha_spell_check

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

awesome-ml-internships

List of companies offering Machine learning and Data Science internships. This list is made by Pratik Ratadiya considering the US market and it may not be inclusive of other companies.

data-science-interview-questions

This repository is created by Youssef Hosni. He is a Ph.D. researcher in the field of computer vision in the center of machine vision and signal analysis (CMVS) at the University of Oulu and a Data Science Instructor & Writer. Medium blog: https://youssefraafat57.medium.com/list/data-science-interview-questions-6789a80bdb14 LinkedIn : https://www.linkedin.com/in/youssef-hosni-b2960b135/

dlrr

DeepSearchRelevanceRanking-KDD2022-Tutorial

fundamentals_of_deep_learning

This repository contains the solutions to all the assignments that were done as part of enhancing the learning experience in the CS6910: Fundamentals of Deep Learning course offered by Indian Institute of Technology Madras. The course was taught by Prof. Mitesh Khapra.

human_judgements

Test Collection

indic_nlp_library

Resources and tools for Indian language Natural Language Processing

interview-ready-system-design-resources

These are the best resources for System Design on the Internet

intro-to-data-science

Back in 2017, Damien Benveniste taught an Introduction to Data Science course. The class was designed for Physics master students but half of the students that signed up for the course were master students from the Stats department. Damien Benveniste really enjoyed teaching that class but that was a lot of work! It has been sitting on my Github for the past 5 years so Damien Benveniste thought Damien Benveniste would make my notes available. Damien Benveniste realized that there are a couple a docs missing and the repo is somewhat a mess, so Damien Benveniste’ll update the repo once Damien Benveniste find the time. You will probably find that the code is outdated but it shouldn't be too hard to update it. The lectures were hands-on coding sessions where Damien Benveniste would take the students through a subject while coding in front of them in a notebook with them testing that same code in real time. Damien Benveniste thought it was important for the students to see me code so Damien Benveniste could take them through debugging my code if Damien Benveniste hit a bug and Damien Benveniste could also come to unblock them if it didn't work for them. Those notebooks may be difficult to follow as they assume Damien Benveniste was explaining while coding. You will find the lecture notebooks here: https://lnkd.in/gmyGyrUf - Exploratory data analysis - Advance Data Manipulation - Data Visualization - Descriptive statistics - Statistical inference - Introduction to probability - Introduction to machine learning - Feature selection and Imputation - Unsupervised Learning - Deep learning (missing for now) Damien Benveniste also had more theoretical notes when the subject was more math involved:https://lnkd.in/gjFPzkht Damien Benveniste spent a lot of time trying to design original hands-on coding Homework: https://lnkd.in/gY4yj63f - Homework 1: Data manipulation with wikipedia data - Homework 2: Building a Social network with Wikipedia data - Homework 3: Statistical study of Particles Diffusion - Homework 4: Statistical study of the Ising Model - Homework 5: a Kaggle-like competition with all the students in the class - Homework 6: Computer vision + Machine Learning You can find some solutions here: https://lnkd.in/gi_fxtPm Damien Benveniste also had 2 midterms that were actually 2 weeks long open book take-home exams. The material were not taught in class prior and they had to find the resource themselves to learn the subjects. They could work together and Damien Benveniste would guide them offline to find the best resources and help them solve the questions. The idea were for the students to solve interview level questions by learning to find the materials online (as we actually do on the job). You can find those 2 Midterms with solutions here: https://lnkd.in/g8WPq-_g . The final was a personal project on any data science subject of their choice.

linked_list

majorproject

The project "Transforming Transactional Marketing of Retailers" was done during my Bachelor's in computer Science & Engineering in Siddaganga Institute of Technology, Tumkur, Karnataka, India as a final year major project.

markdown-cheatsheet

Markdown Cheatsheet for Github Readme.md

new-grad-positions-2023

A collection of New Grad full time roles in SWE, Quant, and PM.

nlp

This repository contains the assignments and project done as part of enhancing the learning experience in the CS6370-Natural Language Processing Course (https://www.cse.iitm.ac.in/course_details.php?arg=MjI=) offered in Indian Institute of Technology Madras by Prof. Sutanu Chakraborti (https://www.cse.iitm.ac.in/~sutanuc/).

nlp_workshop_odsc_europe20

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

open-ended-projects

pattern_recognition_machine_learning

CS5691 - Pattern Recognition and Machine Learning is course in Indian Institute of Technology Madras which was taught by Prof. Arun Rajkumar during Sep2020 -Dec 2020. This repository contains the projects and assignments done as part of learning in the course.

research-literature_study

Literature Documentation

revisiting_anwesha

This work presents four directions to address the current limitations of Anwesha (prototype for Semantic Search in Bangla).

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.