6373 Open source projects that are alternatives of or similar to The Data Science Workshop

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

Learning numerical computing with notebooks for all ages.

Online machine learning methods

Machine Learning Lectures at the European Space Agency (ESA) in 2018

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

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Train multi-task image, text, or ensemble (image + text) models

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Machine Learning algorithms coded from scratch

Vectorized Machine Learning in Python 🐍 From Scratch

Python code for common Machine Learning Algorithms

This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

【运筹OR帷幄|数据科学】pandas教程系列电子书

📚 [.md & .ipynb] Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc. 💫 人工智能与深度学习实战，数理统计篇 | 机器学习篇 | 深度学习篇 | 自然语言处理篇 | 工具实践 Scikit & Tensoflow & PyTorch 篇 | 行业应用 & 课程笔记

DeltaPy - Tabular Data Augmentation (by @firmai)

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

An Interactive Approach to Understanding Deep Learning with Keras

I will update this repository to learn Machine learning with python with statistics content and materials

A Julia machine learning framework

A python library for decision tree visualization and model interpretation.

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

A collection of non-residential buildings for performance analysis and algorithm benchmarking

Misc Statistics and Machine Learning codes in R

A random forest

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

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

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

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

Codice sorgente ed Errata Corrige del mio libro "A tu per tu col Machine Learning"

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

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

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

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

Feature exploration for supervised learning

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Code for Tensorflow Machine Learning Cookbook

Stock for Deep Learning and Machine Learning

The blog post about Kubeflow, including all materials

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

Predicting the probability that a diagnosed breast cancer case is malignant or benign based on Wisconsin dataset

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

A central repository for all my projects

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GitHub Action for repo2docker

Code, Notebooks and Examples from Practical Business Python

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.

Melusine is a high-level library for emails classification and feature extraction "dédiée aux courriels français".

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection