Tag

data

Causal Inference: predicting the cause(s) of the outcome

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.

What is Causal Machine Learning and Why Should You Care?

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.

Managing AI and ML Projects: a primer for success

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.

Get On Board with JAX

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.

Taking Back Control over Your Data

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.

Clustering Data into Groups, Part 3

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

Clustering Data into Groups, Part 2

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

Clustering Data into Groups, Part 1

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

Bias and Fairness in Machine Learning, Part 3: building a bias-aware model

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

Bias and Fairness in Machine Learning, Part 2: building a baseline model and features

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

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