Working with Dataframes in Julia

An excerpt from Julia for Data Analysis by Bogumil Kaminski

This article dives into working with data in dataframes with Julia.

Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.

Julia Crash Course: Dictionaries

An excerpt from Julia as a Second Language by Erik Engheim

This article covers:

·   Storing values on keys in dictionaries.

·   Working with pair objects.

·   Using tuples to create dictionaries.

·   Comparing dictionaries and arrays.

Read it if you’re interested in the Julia language or in how it handles dictionaries.

Vectorizing Your Code using Broadcasting

An excerpt from Julia for Data Analysis by Bogumil Kaminski

Many languages designed for doing data science provide ways to perform vectorized operations, which is also often called broadcasting. In Julia, broadcasting is also supported. In this article you will see how to use it.

Read it if you’re a data scientist or anyone who works with lots of data, and if you’re interested in the Julia language.

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.

It’s Time to Learn about Bayesian Optimization

An excerpt from Bayesian Optimization in Action by Quan Nguyen

What is Bayesian Optimization? What problem(s) does it propose to solve? If you deal with Machine Learning in your job and you’re running into problems with things like black box optimization and hyperparameter tuning, then Bayesian optimization is something you should learn more about.

Read on if you want to learn more. Bayesian optimization isn’t as difficult as you might think!

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.

The Principles of the Data Mesh Architecture

An excerpt from Data Mesh in Action by Jacek Majchrzak, Sven Balnojan, and Marian Siwiak

This excerpt explores the four principles of the Data Mesh as a concept.

Read this article if you are interested in learning what a Data Mesh is and how it is used.

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