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As a Python user, I find the {pySpark} library super handy for leveraging Spark’s capacity to speed up data processing in machine learning projects. But here is a problem: While pySpark syntax is straightforward and very easy to follow, it can be readily confused with other common libraries for datawrangling.
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A prolific educator, Julien shares his knowledge through code demos, blogs, and YouTube, making complex AI accessible. As the author of *Hands-On Data Analysis with Pandas* (now in its second edition), she is a recognized expert in making data actionable.
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