Non-Brand Data helps data professionals learn how to use ML, GenAI, and analytics in their actual work through practical articles, guides, templates, and workflows.
| Platform |  | Pricing | Freemium | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 378 | Founded | 7 years ago | Last Issue | 3 days ago |
| Active | |||||
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Getting Started With TimesFM-3 for Time-Series Forecasting
Time-series forecasting helps us estimate future values so we can plan ahead. For example, we can use historical sales data to predict demand and decide how much inventory to prepare for the coming month. We look for patterns in observation...
Can TabPFN-3.5 Compete With Traditional Machine Learning Models?
Building a machine learning model for tabular data often starts with familiar methods such as Logistic Regression, Random Forest, or CatBoost. We train them on our data and compare their predictions to find a model that suits our problem....
Module 14: Writing the results and recommendations
A SQL result can be correct and still make a weak report. The table gives the numbers, but the reader still needs to know what they mean and what they don't show.
Previous module: Module 13, When an SQL query needs optimization.
Module 13: When a SQL query needs optimization
In this module, we will learn how to read a PostgreSQL query plan and decide whether an index is worth keeping.
Previous module: Module 12, Checking whether your result is right.
Module 12: Checking whether your result is right
Let's learn how to check a SQL result before we use it in a report or recommendation.
Previous module: Module 11, Using window functions without changing the row count.
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Sharing Data Knowledge to improve your values
Outstand using data -- Data Science, Design and Tech Tech Writer @KDnuggets @DataCamp \ud83d\udc49\ud83c\udffbInquiries in [email protected]
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