Pinned Repositories
2017-slides
Slides and talk assets from PyCon 2017
amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
book
A Book about Pythonic Application Architecture Patterns for Managing Complexity. Cosmos is the Opposite of Chaos you see. O'R. wouldn't actually let us call it "Cosmic Python" tho.
PropensityToConvert
Propensity models make true predictions about a customer’s future behavior. With propensity models you can truly anticipate a customer's future behavior. Here we focus on building a combination of a Propensity to convert and a Propensity to buy models that can influence the kind of marketing campaigns we adopt and who we decide to target (predicted converters vs non-converters) leading to spend optimizations that eventually increase the ROI on digital marketing campaigns.
python-tutorials
Various levels of python tutorials
yajnapale's Repositories
yajnapale/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
yajnapale/python-tutorials
Various levels of python tutorials
yajnapale/2017-slides
Slides and talk assets from PyCon 2017
yajnapale/book
A Book about Pythonic Application Architecture Patterns for Managing Complexity. Cosmos is the Opposite of Chaos you see. O'R. wouldn't actually let us call it "Cosmic Python" tho.
yajnapale/PropensityToConvert
Propensity models make true predictions about a customer’s future behavior. With propensity models you can truly anticipate a customer's future behavior. Here we focus on building a combination of a Propensity to convert and a Propensity to buy models that can influence the kind of marketing campaigns we adopt and who we decide to target (predicted converters vs non-converters) leading to spend optimizations that eventually increase the ROI on digital marketing campaigns.