{"product_id":"154677","title":"StatQuest Machine Learning with Pictures ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLQflDGGE1EpKxp4hv87UTzBTD.png?v=1765081309\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e StatQuest Machine Learning with Pictures \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/117173369\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eThere has never been a more visual and intuitive machine learning course.\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eMachine learning is a fascinating and powerful field, but it's also incredibly complex.\u003cbr\u003e This book breaks down complex machine learning algorithms into easy-to-understand chunks and presents them with intuitive examples and diagrams.\u003cbr\u003e Instead of summarizing concepts in words, we explain them in an innovative StatQuest way, making it easy to understand what machine learning is and what its goals are.\u003cbr\u003e Let's build the foundation of machine learning with a visual learning method that explains one picture per page.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e About the Author and Translator 3\u003cbr\u003e Translator's Preface 4\u003cbr\u003e Beta Reader Review 5\u003cbr\u003e How to Read This Book 9\u003cbr\u003e\u003cbr\u003e CHAPTER 1: Basic Machine Learning Concepts!!! 10\u003cbr\u003e CHAPTER 2 CROSS-VALIDATION!!! 23\u003cbr\u003e CHAPTER 3: Basic Statistics Concepts!!! 32\u003cbr\u003e CHAPTER 4: LINEAR REGRESSION!!! 77\u003cbr\u003e CHAPTER 5: Gradient Descent!!! 85\u003cbr\u003e CHAPTER 6: Logistic Regression!!! 110\u003cbr\u003e CHAPTER 7 Naive Bayes!!! 122\u003cbr\u003e CHAPTER 8 Evaluating Model Performance!!! 138 \u003cbr\u003eCHAPTER 9: Preventing Overfitting with Regularization!!! 166\u003cbr\u003e CHAPTER 10: Decision Trees!!! 185\u003cbr\u003e CHAPTER 11: Support Vector Classifiers and Support Vector Machines (SVMs)!!! 220\u003cbr\u003e CHAPTER 12 NEURAL NETWORKS!!! 236\u003cbr\u003e Appendix (something you probably learned in school but have now forgotten)!!! 273\u003cbr\u003e\u003cbr\u003e Acknowledgments 304\u003cbr\u003e Search 306\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4094\/MidCate008\/409377148.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eInto the book\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e Problem: As we will learn later, there are various methods in machine learning for classification or quantitative prediction.\u003cbr\u003e So how do we choose which method to use? For example, let's say we're using this black line to predict height from weight.\u003cbr\u003e Or let's say we want to predict height based on weight with this squiggly green curve. \u003cbr\u003eShould I use the black straight line or the squiggly green curve? Answer: In machine learning, deciding which method to use typically means trying it out and seeing how well it performs.\u003cbr\u003e For example, if this person weighs this much… the black line will predict that this person is this much taller.\u003cbr\u003e Conversely, the squiggly green curve predicts that this person will be slightly taller.\u003cbr\u003e\u003cbr\u003e --- p.14\u003cbr\u003e\u003cbr\u003e There are many machine learning methods that look cool, like deep learning convolutional neural networks.\u003cbr\u003e And every year, many new and interesting methods are released.\u003cbr\u003e But no matter which method you use, the most important thing is that it must perform well on test data. BAM!!! Now that we've covered some key machine learning concepts, let's dive into some fancy machine learning terminology. \u003cbr\u003eIf you remember it well, you will look smart when you attend the dance party.\u003cbr\u003e\u003cbr\u003e --- p.19\u003cbr\u003e\u003cbr\u003e Previously, we looked at how the binomial distribution calculates the probability of a series of binary outcomes, such as the probability that two out of three people prefer pumpkin pie.\u003cbr\u003e However, there are many other discrete probability distributions that are used in various situations.\u003cbr\u003e For example, if you can read 10 pages of this book in an hour on average, you can use the Poisson distribution to calculate the probability that you will read 8 pages in the next hour.\u003cbr\u003e\u003cbr\u003e --- p.48\u003cbr\u003e\u003cbr\u003e ROC stands for receiver operating characteristic. \u003cbr\u003eThis name derives from a graph used to accurately identify fighter jet signals from radar signals received during World War II. ROC graphs are extremely helpful when trying to find good classification thresholds because they summarize at a glance how well each threshold performs in terms of the true positive rate and false positive rate.\u003cbr\u003e\u003cbr\u003e --- p.154\u003cbr\u003e\u003cbr\u003e Let's say we create two models, logistic regression and naive Bayes, and test them on the same data.\u003cbr\u003e We want to know which model performs better.\u003cbr\u003e In theory, we could compare the ROC graphs of each model.\u003cbr\u003e If we only have two models to compare, this might be a good option.\u003cbr\u003e\u003cbr\u003e --- p.160\u003cbr\u003e \u003cbr\u003eProblem: Like linear regression, neural networks have parameters that must be optimized to fit a squiggly curve or curved line to the data.\u003cbr\u003e How can we find the optimal values ​​for these parameters? Answer: Like linear regression, we can use gradient descent or stochastic gradient descent to find the optimal parameter values.\u003cbr\u003e But we won't call it gradient descent.\u003cbr\u003e Then it would be too easy.\u003cbr\u003e We'll call this backpropagation, which comes from the method of finding the derivatives of each parameter in a neural network (going backwards and forwards). BAM!!!\u003c\/div\u003e\n\u003cdiv\u003e --- p.255\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eAn innovative way to understand machine learning more easily than a summary.\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e I want to clearly understand the concepts and terminology of machine learning that I am encountering for the first time.\u003cbr\u003e But just looking at a thick machine learning book gives me a headache, so I don't readily pick it up. \u003cbr\u003eLet's put those books aside for now.\u003cbr\u003e By following the illustrations and fun examples in this book, you can easily and surely learn the fundamentals of machine learning.\u003cbr\u003e\u003cbr\u003e Is there a better book for an introduction to machine learning?\u003cbr\u003e Even those who know nothing about machine learning will quickly understand it if they immerse themselves in the content and read it.\u003cbr\u003e The author's humor, clear answers, and helpful guides like Normalsaurus and Statsquatch guide you from the very basics to advanced topics like neural networks.\u003cbr\u003e\u003cbr\u003e Instead of summarizing concepts in words, author Josh Starmer created the innovative StatQuest method, which has helped people around the world win data science competitions, pass exams, graduate from school, and get jobs and promotions.\u003cbr\u003e I sincerely hope that you, the readers, will be one of them.\u003cbr\u003e I'll conclude this review with a quote from a beta reader. \u003cbr\u003e“I hope that from now on, there will be no one who does not know this book.”\u003cbr\u003e\u003cbr\u003e \u003cb\u003eKey Contents\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e ■ Machine Learning Basics!!!\u003cbr\u003e ■ Cross validation!!!\u003cbr\u003e ■ Basic statistical concepts!!!\u003cbr\u003e ■ Linear regression!!!\u003cbr\u003e ■ Gradient descent!!!\u003cbr\u003e ■ Logistic regression!!!\u003cbr\u003e ■ Naive Bayes!!!\u003cbr\u003e ■ Evaluate model performance!!!\u003cbr\u003e ■ Prevent overfitting with regularization!!!\u003cbr\u003e ■ Decision tree!!!\u003cbr\u003e ■ Support Vector Classifier and Support Vector Machine!!!\u003cbr\u003e ■ Neural network!!! \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e February 16, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 308 pages | 798g | 257*188*15mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791192469805\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1192469801 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893442379818,"sku":"154677","price":37.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/7f38d1a22b8553d74be20a9aa072b48c.jpg?v=1765402317","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154677","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}