The StatQuest Illustrated Guide To Machine Learning Paperback – November 7, 2022

This is a great book! I loved StatQuest and this book is written with the same approach. Lots of pictures. It's easy to understand, and while I'm only half way through it, it has helped my ML understanding tremendously. I had taken a Machine Learning course, but this book explains things so much better than my course text did. Highly recommend.

This THE book to read to understand machine learning. I am so grateful to you, Josh Starmer!

For anyone seeking a intermediate\beginner-friendly and visually engaging introduction to machine learning, Josh Starmer's book is an excellent option. It effectively simplifies complex concepts, presenting them in an easily digestible format, often enhanced by clear illustrations. By minimizing the use of heavy mathematical jargon, the book creates a welcoming environment for readers who may feel intimidated by the subject matter. This approachable style makes it a valuable resource for newcomers to the field.

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 😉.

The author has a clear understanding of what steps are needed to systematically teach ML. The illustrations and the arrows navigate the user through them while teaching concepts and building a strong foundation. Kids can understand these concepts more naturally since they study similar topics daily. But more importantly, the adults who have no domain expertise can also benefit immensely from this style of teaching. I am going to recommend this book to anyone interested in learning about ML

If you want to understand key concepts in statistics, and you prefer visual explanations, look no further, this book will save you tons of time. Also complemented by the videos online. Awesome, just awesome!

The corners of the book came slightly warn.

Best book to learn machine learning from basics ro complex algorithms. Josh have teaches the most complex algorithms in very simple way. Even a beginner with no knowledge can study it.