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

Codes and dashboards for 4th place solution for Kaggle's Home Credit Default Risk competition

Modern Data Science

Temporary home for fastai v2 while it's being developed

Learn advanced data science on real-life, curated problems

深度学习在推荐系统中的应用及论文小结。

Notes, examples, and Python demos for the textbook "Machine Learning Refined" (published by Cambridge University Press).

Quiz & Assignment of Coursera

Python pour Statistique et Science des Données -- Syntaxe, Trafic de Données, Graphes, Programmation, Apprentissage

A collection of notebooks of my Machine Learning class written in python 3

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

Talks & Workshops by the CODAIT team

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

Automatic extraction of relevant features from time series:

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

Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

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

Introduction to Data Science

Feature exploration for supervised learning

Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017

Create HTML profiling reports from pandas DataFrame objects

Python training for business analysts and traders

ML.NET Workshop to predict car sales prices

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

Resources for weekly Data Science Lunch & Learns

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

USC urban data science course series with Python and Jupyter

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

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.

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

📘 The interactive computing suite for you! ✨

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

An ongoing list of pandas quirks

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 ;)

IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018

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.

The Hitchhiker's Guide to Data Science for Social Good

Support content for my blog

Python tools for data analysis

The "Python Machine Learning (2nd edition)" book code repository and info resource

Completed assignments and coding challenges from the Lambda School Data Science program.

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

Google Colaboratory Notebooks and Repositories (by @firmai)

PyMC3 educational resources

Association Rule Mining from Spatial Data for Crime Analysis

The blog post about Kubeflow, including all materials

Dockerfile of Jupyter (IPython notebook) and IOCaml (OCaml kernel) with libraries for data science and machine learning

brief introduction to Python for machine learning

A Julia machine learning framework

Introduction to pandas Treehouse course

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.

My solution to the book A Collection of Data Science Take-Home Challenges

Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge

Python Helper library for Jupyter Notebooks

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

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

Human Activity Recognition (HAR) with Keras and CoreML

🚚 Agile Data Preparation Workflows made easy with dask, cudf, dask_cudf and pyspark

Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

A workshop on data privacy methods for data scientists.