To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these 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 the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
3 有用 o` 2021-03-17 16:34:33
所谓 from scratch 就是作者不使用 sklearn 等库,教读者从头实现常用算法来理解背后的原理。但我认为数据科学的学习应该是从上至下的,先摸数据,建立直觉,反复实操,然后再逐步深入实现。否则一来容易失去兴趣,二来容易眼高手低:虽然学会了算法的朴素实现,但遇到实际问题时却无从下手。
0 有用 綦 2020-03-07 07:58:28
On how to talk to our data scientists more sensibly.
0 有用 小盼 2020-12-26 22:33:39
整本书没有用到sklearn, numpy这些库,纯python实现算法,真的是from scratch; 虽然例子简单,但是对于初学者足够了,比如书中大都是binary的例子,google一下就可以探索multinominal的情况;缺点就是代码散落在各文件处,引用有点凌乱,但是在学习的时候可以全写在一个jupyter notebook中解决。 Deep Learning, Decision T... 整本书没有用到sklearn, numpy这些库,纯python实现算法,真的是from scratch; 虽然例子简单,但是对于初学者足够了,比如书中大都是binary的例子,google一下就可以探索multinominal的情况;缺点就是代码散落在各文件处,引用有点凌乱,但是在学习的时候可以全写在一个jupyter notebook中解决。 Deep Learning, Decision Tree, Application待读; 总体来说结合PRML来看,一本介绍数学理论,一本用代码帮助理解,读完会对机器学习有了基本且清晰的认识 (展开)
0 有用 Lebum 2023-06-06 01:44:32 福建
很多人没明白入门是什么意思, 所谓的入门, 你之前至少要看过: Head First Statistic Computer Science: An Overview Operating System Concepts Computer Networking: A Top Down Approach Introducing Python Fluent Python 这是 O' Reilly 出版的... 很多人没明白入门是什么意思, 所谓的入门, 你之前至少要看过: Head First Statistic Computer Science: An Overview Operating System Concepts Computer Networking: A Top Down Approach Introducing Python Fluent Python 这是 O' Reilly 出版的书, 如果是想打 CS 基础, 出门左转 Pearson 出版社 (展开)
0 有用 Lebum 2023-06-06 01:44:32 福建
很多人没明白入门是什么意思, 所谓的入门, 你之前至少要看过: Head First Statistic Computer Science: An Overview Operating System Concepts Computer Networking: A Top Down Approach Introducing Python Fluent Python 这是 O' Reilly 出版的... 很多人没明白入门是什么意思, 所谓的入门, 你之前至少要看过: Head First Statistic Computer Science: An Overview Operating System Concepts Computer Networking: A Top Down Approach Introducing Python Fluent Python 这是 O' Reilly 出版的书, 如果是想打 CS 基础, 出门左转 Pearson 出版社 (展开)
3 有用 o` 2021-03-17 16:34:33
所谓 from scratch 就是作者不使用 sklearn 等库,教读者从头实现常用算法来理解背后的原理。但我认为数据科学的学习应该是从上至下的,先摸数据,建立直觉,反复实操,然后再逐步深入实现。否则一来容易失去兴趣,二来容易眼高手低:虽然学会了算法的朴素实现,但遇到实际问题时却无从下手。
0 有用 小盼 2020-12-26 22:33:39
整本书没有用到sklearn, numpy这些库,纯python实现算法,真的是from scratch; 虽然例子简单,但是对于初学者足够了,比如书中大都是binary的例子,google一下就可以探索multinominal的情况;缺点就是代码散落在各文件处,引用有点凌乱,但是在学习的时候可以全写在一个jupyter notebook中解决。 Deep Learning, Decision T... 整本书没有用到sklearn, numpy这些库,纯python实现算法,真的是from scratch; 虽然例子简单,但是对于初学者足够了,比如书中大都是binary的例子,google一下就可以探索multinominal的情况;缺点就是代码散落在各文件处,引用有点凌乱,但是在学习的时候可以全写在一个jupyter notebook中解决。 Deep Learning, Decision Tree, Application待读; 总体来说结合PRML来看,一本介绍数学理论,一本用代码帮助理解,读完会对机器学习有了基本且清晰的认识 (展开)
0 有用 綦 2020-03-07 07:58:28
On how to talk to our data scientists more sensibly.