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Chargement... Data Science from Scratch: First Principles with Python (édition 2019)par Joel Grus (Auteur)
Information sur l'oeuvreData Science from Scratch: First Principles with Python par Joel Grus
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Inscrivez-vous à LibraryThing pour découvrir si vous aimerez ce livre Actuellement, il n'y a pas de discussions au sujet de ce livre. I read this prior to beginning an MSc in Data Science and found it to be a great introduction to data science, starting out with the very basics before moving into more general ML techniques and finishing up with some of the more complex topics such as MapReduce. Not an in-depth textbook by any means, but I do not think that is the purpose of this book, moreover to give the reader a well-rounded idea of the field. This is a very basic into topics in statistics and machine learning built around functioning code to perform (some of!) the tasks and algorithms discussed. As an introduction it seemed very solid. I was looking for something a little more in depth, so this was not really the book I was looking for. What am I looking for? Something that bridges between a working knowledge of e.g. some methods in scikit learn to e.g. coding those methods, from scratch. Gradient descent and PCA are covered, but the book stops precisely at 'more interesting'/complex methods e.g. ridge regression/Lasso, and never even touches on e.g. ICA. So, 3-ish stars for me. Maybe 4 stars if you are getting your feet wet for the first time. As bibliotecas, estruturas, módulos e kits de ferramentas do data science são ótimas para desempenhá-lo mas, também, são uma ótima forma de mergulhar na disciplina sem ter, de fato, que entender data science. Neste livro, você aprenderá como os algoritmos e as ferramentas mais essenciais de data science funcionam ao implementá-los do zero.Se você tiver aptidão para matemática e alguma habilidade para programação, o autor Joel Grus lhe ajudará a se sentir confortável com matemática e estatística nos fundamentos de data science. Você precisará iniciar como um cientista de dados com habilidades de hackers. Atualmente, a grande massa de dados contém respostas para perguntas que ninguém nunca pensou em perguntar. Este guia fornece o conhecimento para desenterrar tais respostas.Obtenha um curso intensivo em Python;Aprenda o básico de álgebra linear, estatística e probabilidade ― e entenda como e quando eles são usados em data science;Colete, explore, limpe, mude e manipule dados;Vá fundo nos princípios do aprendizado de máquina;Implemente modelos como k-vizinhos mais próximos, Naive Bayes, regressão logística e linear, árvores de decisão, redes neurais e agrupamentos;Explore sistemas recomendados, processamento de linguagem natural, análise de rede, MapReduce e bases de dados. DEPOIMENTO:“Joel lhe leva em uma jornada desde a curiosidade sobre dados até a completa compreensão de algoritmos que todo cientista de dados deveria ter.”―Rohit Sivaprasad, Cientista de Dados na Soylent aucune critique | ajouter une critique
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data h Aucune description trouvée dans une bibliothèque |
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Google Books — Chargement... GenresClassification décimale de Melvil (CDD)005.133Information Computer Science; Knowledge and Systems Computer programming, programs, data, security Programming Languages General Programming LanguagesClassification de la Bibliothèque du CongrèsÉvaluationMoyenne:
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I didn't finish it.
Not even close.
It might not be bad but I'm not gonna become a programmer... ( )