I easily recommend the book over the video lectures. Do check it out if you are buying the book.
AN INTRODUCTION TO STATISTICAL LEARNING AMAZON FOR FREE
The Mooc is available for free from Stanford Lagunita. The word was coined by them, to include all methods of learning from data excluding neural networks (which came from the AI world). They are pioneers in the field of statistical learning. It will be one of the books in my collection. It was made of silky paper and page turning was so easy. The copy I got from Springer was simply a delight to read. I will advise authors for another edition where the additions/editing are done by one author throughout and there are tougher parts put in the appendix. The consistency is not there.ģ) It felt like different chapters in the book were written by different people and that's why there is a difference in the level of mathematics used and tone of teaching used. Just that there are other parts of the book where they do not assume the same level of expertise from the reader and will just state a complex formula without derivation or justification. Several times the summations used take some disambiguation to understand. They don't treat the reader with baby gloves. To not assume no knowledge of mathematics by the reader, especially given -Ģ) The book is mathematically hard in parts.
My advise to the authors is cover a topic fully or not at all. This to me is the same mistake made by several Indian books, and unlike the authors' other book ESL.
This book leaves you in the wilderness of mathematics many times of stating a conclusion without a proof. The reason for the lower rating is that there were many parts where i looking for external references. For the first time in my life (I've been in data science for ten odd years) I learnt about t-statistic and f-statistic in the way that it should be taught.Ģ) Good mathematical coverage of cross validationģ) Good coverage of Logistic Regression, PCA, Random Forest and trees, Clustering.īonus: Great coverage of relationship between SVM and logistical regression - history of hype behind kernel methods in SVMġ) I mentioned in my headline that i have a love hate relationship with this book. Kinds of tools covered: Regressions, Logistical Regression, Linear discriminant analysis, Decision Trees, Random Forest, Boosting, Cross Validation, SVM, PCA, K-means clustering.ġ) Great coverage in Linear Regression. Includes most general techniques in AI excluding Neural Networks. If you self-assess yourself to be good at Maths and an advanced learner, I would recommend the authors' other book Elements of Statistical Learning.Ī set of tools used to analyze data. So for those learners I would recommend this book. This is especially true for CS students who learn more of discrete math which doesn't lend itself well in the world of AI/ML. You have to have you statistical bases covered before you touch the more advanced topics. This is not the correct way to enter the field. Since data science is a fast moving field, there are people who want to jump on the deep learning bandwagon straightaway. Para obtener más información sobre cómo y para qué fines Amazon utiliza la información personal (como el historial de pedidos de Amazon Store), visita nuestro Aviso de privacidad. Para ello, visita Preferencias de cookies, tal y como se describe en el Aviso de cookies. Puedes cambiar de opinión en cualquier momento. Haz clic en “Personalizar cookies” para rechazar estas cookies, tomar decisiones más detalladas u obtener más información.
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