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awesome-computer-vision

A curated list of awesome computer vision resources

awesome-explainable-ai

A collection of research materials on explainable AI/ML

blueprints-text

Jupyter notebooks for our O'Reilly book "Blueprints for Text Analysis Using Python"

brew

🍺 The missing package manager for macOS (or Linux)

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

chatgpt-prompt-engineering

Jupyter code notebooks of "ChatGPT Prompt Engineering for Developers" by DeepLearning.AI and OpenAI.

comp1

Econ 626 - competition 1 (q1 and q4)

comp2

competition 2 - Econ 626 - Lasso Boston housing

ddsr

Source code for the book "Doing Data Science in R" by Mark Andrews (SAGE publishers, 2021)

deep-learning-for-causal-inference

Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2.

drwhy

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

econ626competitions

explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]

hands-on-explainable-ai-xai-with-python

Explainable AI with Python, published by Packt

hundred-days-of-python

100 Days of Python

interactive-dashboards-and-data-apps-with-plotly-and-dash

Interactive Dashboards and Data Apps with Plotly and Dash, published by Packt

islr

Introduction to Statistical Learning using R - Exercise solutions

leetcode

😏 LeetCode solutions in any programming language | 多种编程语言实现 LeetCode、《剑指 Offer（第 2 版）》、《程序员面试金典（第 6 版）》题解

machine-learning-projects

A collection of ML projects

ml-notebooks

:fire: A series of code examples for all sorts of machine learning tasks and applications.

ml_process_course

Public repo for the 365 Data Science ML Process Course

mlcourse

Machine learning course materials.

msci253

Lab Sessions for my TA Job, MSCI 253 - Statistics and Probability II

msci719

neural-networks-and-deep-learning

Code samples for my book "Neural Networks and Deep Learning"

nlp_course

YSDA course in Natural Language Processing

phd-task1work

All code related to task 1 for my PhD

piml-toolbox

PiML (Python Interpretable Machine Learning) toolbox for model development and validation

pscyh632

Multiple Regression Course

pyprobml

Python code for "Probabilistic Machine learning" book by Kevin Murphy