From Causal Inference in Data Science by Aleix Ruiz de Villa
Causal inference models predict why something will happen, i.e. causal effects, rather than the outcomes themselves. This is useful in many instances and is a budding field in machine learning and data science.
Read on to see how it works and what you will learn from this book.
From Causal Machine Learning by Robert Ness
Enhance machine learning with causal reasoning to get more robust and explainable outcomes. Power causal inference with machine learning to create next gen AI..There has never been a better time to get into building causal AI.
Read on for more.
An excerpt from Managing Machine Learning Projects by Simon Thompson
Managing Machine Learning Projects will teach you to guide machine learning projects from design to production—no machine learning experience required!
Read this article if you’re a project manager who works with machine learning applications.
An excerpt from JAX in Action by Grigory Sapunov
This excerpt covers:
What is JAX, and how does it compare to NumPy
Why use JAX?
Comparing JAX with TensorFlow/PyTorch
Read it if you’re a Python developer or machine learning practitioner who is interested in JAX and what it can do.
An excerpt from Data for All by John K Thompson
Want to stop being a passive source of free data that other people make money off of? This book is a must-read for anyone who wants to take control of their personal data. It lays out how businesses collect, use, and exploit your data (and the related dangers), and clearly explains the legislation that will overturn the existing system, and how you can use it.
It’s time to completely change your relationship with your data.
From Data Science Bookcamp by Leonard Apeltsin
This 3-part article series covers:
Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas
From Data Science Bookcamp by Leonard Apeltsin
This 3-part article series covers:
Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas
From Data Science Bookcamp by Leonard Apeltsin
This 3-part article series covers:
Clustering data by centrality
Clustering data by density
Trade-offs between clustering algorithms
Executing clustering using the scikit-learn library
Iterating over clusters using Pandas
From Feature Engineering Bookcamp by Sinan Ozdemir
This article series covers
● Recognizing and mitigating bias in our data and model
● Quantifying fairness through various metrics
● Applying feature engineering techniques to remove bias from our model without sacrificing model performance
From Feature Engineering Bookcamp by Sinan Ozdemir
This article series covers
● Recognizing and mitigating bias in our data and model
● Quantifying fairness through various metrics
● Applying feature engineering techniques to remove bias from our model without sacrificing model performance