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mlops-mini-projects

Explore MLOps excellence! This repository curates mini-projects demonstrating ML deployment, NLP, and Deep Learning. Discover CI/CD/CT pipelines, best practices, and dive into practical MLOps insights. Elevate your skills in deploying and managing cutting-edge machine learning applications.

mlops_workshop

Azure MLOps

mmmflask

modeldeployment

modelDeployment

modern-sota-cnn-architectures

newleadstatusmlmodeldeployment

nlp-interview-preparation

Welcome to the NLP Interview Preparation repository! Discover a curated set of interview questions and answers for Data Scientists. Elevate your understanding of Natural Language Processing, navigate technical interviews confidently, and succeed in the dynamic field of data science with a focus on NLP applications.

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

open-data

personalized-medicine-redefining-cancer-treatment-project

A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated. But this is only partially happening due to the huge amount of manual work still required. Memorial Sloan Kettering Cancer Center (MSKCC) launched this competition, accepted by the NIPS 2017 Competition Track, because we need your help to take personalized medicine to its full potential. Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers). Currently this interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature. For this competition MSKCC is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations. We need your help to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations.ubmissions are evaluated on Multi Class Log Loss between the predicted probability and the observed target. Submission File For each ID in the test set, you must predict a probability for each of the different classes a genetic mutation can be classified on. The file should contain a header and have the following format:

praveen76

predict-employee-attrition-using-advanced-algorithms

predict-loan-eligibility-using-ibm-watson-studio

Build a predictive model to automate the process of targeting the right applicants

pull-requests

In this repository we will create different pull requests

python-coding-interview-preparation

Explore the Python Coding Interview Prep repository – a curated collection of interview questions and answers for data scientists. Enhance your Python skills, navigate technical interviews confidently, and prepare for success in the dynamic field of data science!!

python-for-algorithms--data-structures--and-interviews

Files for Udemy Course on Algorithms and Data Structures

r-server-campaign-optimization

Campaign optimization solution with SQL Server ML Services

ratelockdateratepredictionmodel

recommendation-system-interview-prep-guide

Explore the Recommendation System Interview Prep Guide! This GitHub repository provides curated interview questions and answers for Data Scientists. Elevate your knowledge of recommendation systems, navigate technical interviews with confidence, and succeed in the dynamic field of data science focused on recommendation system applications.

recommendation_system

recommender-system-and-convai-using-llms-on-e-commerce-data

Transform e-commerce using Amazon's sales data. Develop a Recommender System and Conversational AI for tasks like Sentiment Analysis, product summarization, and detailed product retrieval. Elevate user experience with advanced Large Language Models (LLMs).

robyn

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency a

smartflaskapp

Jenkins CICD devops

sms-spam-collection

sql-interview-questions

statistics-interview-preparation-guide

Explore the Statistics Interview Preparation Guide! This GitHub repository offers a curated collection of interview questions and answers tailored for Data Scientists. Elevate your statistical knowledge, confidently navigate technical interviews, and prepare for success in the dynamic field of data science with a focus on statistical applications.

testing-and-monitoring-ml-deployments

Example project for the course "Testing & Monitoring Machine Learning Model Deployments"

timeseriesforecastingmodel

titanic-case-study

udemyflaskapputest