Storytelling in Machine Learning: Before and After the Model
Data Science: The Hard Parts by Daniel Vaughan (ISBN 978-1-098-14647-4)
← Previous: Productionizing ML Models | Next: From Prediction to Decisions
Machine learning
Data Engineering With AWS Chapter 13: Enabling AI and Machine Learning
This is post 19 in my Data Engineering with AWS retelling series.
Throughout this book, we have been ingesting data, transforming data, storing data, querying data, and visualizing data. All of that is incredibly useful on its own. But there is a whole other level where data gets really powerful: when you use it to teach a machine to make predictions.
Data Science Foundations Chapter 14: Machine Learning and AI Explained Simply
Everyone has an opinion about AI. Your coworker worries robots will take his job. Your cousin swears ChatGPT wrote his college essay. Chapter 14 of Data Science Foundations by Stephen Mariadas and Ian Huke explains what machine learning and artificial intelligence actually are. And how data science connects to all of it.
Python vs R Workflows - Machine Learning, Visualization, and More
Chapter 5 is where Boyan Angelov gets practical about the question everyone dances around: which language should you actually use for which job?
Data Science Foundations Chapter 10 Part 2: Time Series, Classification, and Clustering Models
This is Part 2 of 2 for Chapter 10. In Part 1 we covered how to pick the right model and looked at regression. Now we get into the rest: time series, classification, clustering, and association analysis.
Data Engineering With GCP Chapter 8 Part 2: Vertex AI, AutoML, and ML Pipelines
Part 1 covered the ML basics: what supervised and unsupervised learning are, how a simple model gets trained, and why data engineers should care about ML at all. Now in Part 2, Adi Wijaya moves into the GCP tools that make ML actually work in production. This is where theory meets infrastructure.
Data Engineering With GCP Chapter 8 Part 1: Machine Learning Basics for Data Engineers
Chapter 8 is the one where Adi Wijaya finally brings up the topic every data engineer either loves or dreads: machine learning. And honestly, he does a good job of calming down both camps. If you are excited about ML, great. If you think it has nothing to do with your job, think again. This chapter shows why ML and data engineering are way closer than most people realize.