6653 Open source projects that are alternatives of or similar to Flaml

Home assignments for data science positions

A specially designed light version of Fast AutoAugment

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

Aulas da Escola de Inteligência Artificial de São Paulo

Extensive and accessible COVID-19 data + forecasting for counties and hospitals. 📈

👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋

The code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

[tutorial]A functional, Data Science focused introduction to Python

Materials for GWU DNSC 6279 and DNSC 6290.

An open source python library for automated feature engineering based on Genetic Programming

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

Codebase for "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization", ICML 2018.

CardIO is a library for data science research of heart signals

Virgilio is developed and maintained by these awesome people. You can email us virgilio.datascience (at) gmail.com or join the Discord chat.

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

Human-explainable AI.

Automated modeling and machine learning framework FEDOT

Cryptocurrency Price Prediction Using LSTM neural network

Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog

Data Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Compilation of R and Python programming codes on the Data Professor YouTube channel.

Web scrapping and related analytics using Python tools

This is Andrew NG Coursera Handwritten Notes.

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Open-Source Tutorial For Analyzing and Visualizing Cryptocurrency Data

open source for wechat-official-account (ID: PyDataLab)

Jupyter notebooks from the scikit-learn video series

Extração de dados da API do CartolaFC, análise exploratória dos dados e modelos preditivos em R e Python - 2014-20. [EN] Data munging, analysis and modeling of CartolaFC - the most popular fantasy football game in Brazil and maybe in the world. Data cover years 2014-19.

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications

Interactive reports to analyze machine learning models during validation or production monitoring.

A minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch

Fast and flexible AutoML with learning guarantees.

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

experiments with python

[ISBI'21] MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

SuperComputing 2017 Deep Learning Tutorial

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

A collection of demos showcasing automated feature engineering and machine learning in diverse use cases

Production Data Science: a workflow for collaborative data science aimed at production

H2O.ai Machine Learning Interpretability Resources

A simple probabilistic programming language.

Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

Quiz & Assignment of Coursera

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain

a delightful machine learning tool that allows you to train, test, and use models without writing code

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Course materials for Dartmouth Course: Storytelling with Data (PSYC 81.09).

Jupyter notebook and datasets from the pandas Q&A video series

Time Series Forecasting Best Practices & Examples

Amacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.

A day to day plan for this challenge. Covers both theoritical and practical aspects

Kotlin API for Lets-Plot - an open-source plotting library for statistical data.

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.