ztiggy

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

AmazonSageMakerCourse

SageMaker Course Material

building-machine-learning-pipelines

Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson

Churn_Causality_Analysis

DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Hands-On-Data-Analysis-with-Pandas-2nd-edition

Materials for following along with Hands-On Data Analysis with Pandas – Second Edition

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ml-design-patterns

Source code accompanying O'Reilly book: Machine Learning Design Patterns

workshop

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

ztiggy/amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

ztiggy/AmazonSageMakerCourse

SageMaker Course Material

ztiggy/DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

ztiggy/Hands-On-Data-Analysis-with-Pandas-2nd-edition

Materials for following along with Hands-On Data Analysis with Pandas – Second Edition

ztiggy/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ztiggy/workshop

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

ztiggy/building-machine-learning-pipelines

Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson

ztiggy/Churn_Causality_Analysis

ztiggy/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

ztiggy/ml-design-patterns

Source code accompanying O'Reilly book: Machine Learning Design Patterns

ztiggy/Marketing-Attribution-Models

Python Class created to address problems regarding Digital Marketing Attribution.

ztiggy/Marketing-automation

Marketing automation with data science

ztiggy/mlcourse.ai-notebook

Open Machine Learning Course mlcourse.ai

ztiggy/py4e

Web site for www.py4e.com and source to the Python 3.0 textbook

ztiggy/Python-MLOps-Cookbook

This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.

ztiggy/real-world-machine-learning

Code accompanying the Real-World Machine Learning book

ztiggy/sagemaker-explaining-credit-decisions

Amazon SageMaker Solution for explaining credit decisions.