Kauvin Lucas's starred repositories
free-for-dev
A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
100-Days-Of-ML-Code
100 Days of ML Coding
Data-Science-For-Beginners
10 Weeks, 20 Lessons, Data Science for All!
brave-browser
Brave browser for Android, iOS, Linux, macOS, Windows.
deep-learning
Repo for the Deep Learning Nanodegree Foundations program.
dash-sample-apps
Open-source demos hosted on Dash Gallery
yt-channels-DS-AI-ML-CS
A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
kuwala
Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times
bodywork-core
ML pipeline orchestration and model deployments on Kubernetes.
ungoogled-chromium-archlinux
Arch Linux packaging for ungoogled-chromium
pyspark-churn-prediction
Churn Prediction with PySpark using MLlib and ML Packages
Spark-StudyClub
Grupo de Estudios de Apache Spark organizado por la comunidad Data Engineering Latam
Microsoft-Power-BI-Data-Analyst-Certification-Guide
Microsoft Power BI Data Analyst Certification Guide, published by Packt
maven-unicorn-challenge
This is a web app made with Python consisting of a dashboard that was used as submission for a visualization challenge called "Maven Unicorn Challenge" by Maven Analytics
dais-2021-demo
Data + AI Summit 2021 demo notebooks
spark-kubernetes
This repository contains files used to build images to deploy Spark clusters on Kubernetes
Optimizing-a-Pipeline-in-Azure
The main goal of this project was to build and optimize an Azure ML pipeline using the Python SDK and a provided Scikit-learn Logistic Regression model to solve a classification problem. Hyperdrive was used to optimize the model. This was then compared to an Azure AutoML run to see which of these approaches returns the best tuned model.
Spark-StudyClub
#DataEngineeringLATAM
shieldy_bot
@shieldy_bot Telegram bot repository
big-data-science-notes
My notes of each module in Big Data Science, an online course offered by Semantix Brasil
Album-Review-Ratings
In this project I used a compilation of Album Reviews Ratings made by Kauvin Lucas at Kaggle to do an Exploratory Data Analysis and answer some questions about the data.