7417 Open source projects that are alternatives of or similar to Awesome Google Colab

Allstate Kaggle Competition ML Capstone Project

Best Practices, code samples, and documentation for Computer Vision.

Introduction to pandas Treehouse course

Ways of doing Data Science Engineering and Machine Learning in R and Python

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

This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.

This is Andrew NG Coursera Handwritten Notes.

Open-Source Tutorial For Analyzing and Visualizing Cryptocurrency Data

Tutorial Sessions for SciPy Con 2019

Tutorial: Web scraping in Python with Beautiful Soup

Tutorials and training material for the H2O Machine Learning Platform

Human Activity Recognition (HAR) with Keras and CoreML

An intro to Python & programming for wanna-be data scientists

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

SuperComputing 2017 Deep Learning Tutorial

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

Source material for Python Like You Mean it

Jupyter notebooks from the scikit-learn video series

Best Practices on Recommendation Systems

A Chinese translation of Jake Vanderplas' "Python Data Science Handbook". 《Python数据科学手册》在线Jupyter notebook中文翻译

Machine Learning and Data Analysis Coursera Specialization from Yandex and MIPT

Quiz & Assignment of Coursera

This repository provides everything you need to get started with Python for (social science) research.

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

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

A workshop on data privacy methods for data scientists.

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

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

Data Science Best Practices with pandas

Quiz & Assignment of Coursera

The 3rd edition of course.fast.ai

DeepLearning.ai课程学习Jupyter Notebook作业

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

coursera吴恩达机器学习课程作业自写Python版本+Matlab原版

Introducing neural networks to predict stock prices

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

This is a repository of a topic-centric public data sources in high quality for Recommender Systems (RS)

This is a collection of all the code that can be found on my YouTube channel Sigma Coding.

Temporary home for fastai v2 while it's being developed

深度学习笔记

The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)

📘 The interactive computing suite for you! ✨

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.

All course materials for the Zero to Mastery Machine Learning and Data Science course.

Automatic extraction of relevant features from time series:

A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep

An ongoing list of pandas quirks

Feature exploration for supervised learning

The AI Application Platform We All Need. Human AND Machine Intelligence. Based on experience building AI solutions at Panasonic: robotics predictive maintenance, cold-chain energy optimization, Gigafactory battery mfg, avionics, automotive cybersecurity, and more.

Deep Learning Specialization by Andrew Ng on Coursera.

Early stopping for PyTorch

深度学习在推荐系统中的应用及论文小结。

Support content for my blog

An introduction to network analysis and applied graph theory using Python and NetworkX

The Hitchhiker's Guide to Data Science for Social Good