🕧Get The StatQuest Illustrated Guide To Machine Learning by Josh Starmer PhD
The StatQuest Illustrated Guide To Machine Learning by Josh Starmer PhD
Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each concept is clearly illustrated to provide you, the reader, with an intuition about how the methods work that goes beyond the equations alone. The StatQuest Illustrated Guide does not dumb down the concepts. Instead, it builds you up so that you are smarter and have a deeper understanding of Machine Learning. The StatQuest Illustrated Guide to Machine Learning starts with the basics, showing you what machine learning is and what are its goals, and builds on those, one picture at a time, until you have mastered the concepts behind self driving cars and facial recognition. Read more
I recently picked up a copy of Joshua's new book "The StatQuest Illustrated Guide to Machine Learning" (SIGML). And I must say, I'm very impressed 🤯! By a large margin, it is the most accessible book on ML that I've encountered...the anthesis of a typical dry, esoteric, & unintuitive ML book. Many technical and academic books alienate a large portion of their readers, self-sabotaging their educational value to those would-be learners by employing an esoteric vocabulary that's only accessible to people who possess specific academic backgrounds. By contrast, rather than making assumptions, Joshua takes pains to equip readers with a working knowledge of the language required for the concepts that he introduces - an approach that the entire technical and academic publishing sphere could learn a great deal from (i.e., focus less on sounding smart and more on helping people of all backgrounds to learn effectively)! Along this vein, I was surprised to learn something new in the very first chapter 😊! Many years ago, when first learning spreadsheets, I was introduced to data in rows and columns. Then, I moved on to structured databases with "tabular" data where the rows were referred to as "records" and the columns were referred to as "attributes". Later, in the ML space, I once again encountered tabular data...and this time the rows were called "observations" and the columns were called "variables" (which can be "independent" if they're informing the prediction or "dependent" if they're what's being predicted). By the time that I got into learning Stats/ML, I was mostly just amused to find yet another set of nomenclature for tabular data. I don't recall ever reading or being told why the fields of Stats & ML refer to the columns as variables (the discussion always focused on the independent vs. dependent part). So, without thinking about it much, I just accepted that in the ML context columns are called variables. Yet, here Joshua thoughtfully takes time to explain that columns of data are referred to as variables because the data "varies" from one observation to the next 💡. While completely logical, I have to admit being ignorant about this rationale for the nomenclature until yesterday when skimming through Chapter 1 of SIGML. This is a great book for those who're looking for a gentle, fully accessible introduction to ML that doesn't cut corners...it's also a good resource for seasoned ML practitioners who might want to go back and inspect their knowledge base for unrealized blind spots from a new, more illustrated perspective 😉.
ASIN -> B09ZCKR4H6 Publisher -> Independently published (May 2, 2022) Language -> English Paperback -> 305 pages ISBN-13 -> 979-8811583607 Item Weight -> 1.19 pounds Dimensions -> 8.5 x 0.72 x 6.5 inches Best Sellers Rank: #6,940 in Books (See Top 100 in Books) #3 in Computer Neural Networks #8 in Artificial Intelligence & Semantics
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