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[书籍介绍] Building Machine Learning Systems with Python [推广有奖]

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307Hotel 发表于 2019-5-14 14:48:39 |显示全部楼层
着手搭建机器学习model入门好书~

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Building.Machine.Learning.Systems.with.Python.pdf

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Machine Learning

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stata SPSS
hifinecon 发表于 2019-5-14 18:12:45 来自手机 |显示全部楼层
307Hotel 发表于 2019-5-14 14:48
着手搭建机器学习model入门好书~

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zhaosl 发表于 2019-5-14 22:09:04 |显示全部楼层
https://bbs.pinggu.org/thread-6691294-1-1.html

人家第三版才只要10个币哈。
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yunnandlg 在职认证  学生认证  发表于 2019-5-14 22:40:17 |显示全部楼层
First published: July 2013
What this book covers
Chapter 1, Getting Started with Python Machine Learning, introduces the basic ideaof machine learning with a very simple example. Despite its simplicity, it willchallenge us with the risk of overfitting.
Chapter 2, Learning How to Classify with Real-world Examples, explains the use of real data to learn about classification, whereby we train a computer to be able todistinguish between different classes of flowers.
Chapter 3, Clustering – Finding Related Posts, explains how powerful thebag-of-words approach is when we apply it to finding similar posts withoutreally understanding them.
Chapter 4, Topic Modeling, takes us beyond assigning each post to a single clusterand shows us how assigning them to several topics as real text can deal withmultiple topics.
Chapter 5, Classification – Detecting Poor Answers, explains how to use logisticregression to find whether a user's answer to a question is good or bad. Behindthe scenes, we will learn how to use the bias-variance trade-off to debug machinelearning models.
Chapter 6, Classification II – Sentiment Analysis, introduces how Naive Bayesworks, and how to use it to classify tweets in order to see whether they arepositive or negative.
Chapter 7, Regression – Recommendations, discusses a classical topic in handlingdata, but it is still relevant today. We will use it to build recommendationsystems, a system that can take user input about the likes and dislikes torecommend new products.
Chapter 8, Regression – Recommendations Improved, improves our recommendationsby using multiple methods at once. We will also see how to build recommendationsjust from shopping data without the need of rating data (which users do notalways provide).
Chapter 9, Classification III – Music Genre Classification, illustrates how if someone hasscrambled our huge music collection, then our only hope to create an order is to leta machine learner classify our songs. It will turn out that it is sometimes better totrust someone else's expertise than creating features ourselves.
Chapter 10, Computer Vision – Pattern Recognition, explains how to apply classificationsin the specific context of handling images, a field known as pattern recognition.C
hapter 11, Dimensionality Reduction, teaches us what other methods existthat can help us in downsizing data so that it is chewable by our machinelearning algorithms.
Chapter 12, Big(ger) Data, explains how data sizes keep getting bigger, and howthis often becomes a problem for the analysis. In this chapter, we explore someapproaches to deal with larger data by taking advantage of multiple core orcomputing clusters. We also have an introduction to using cloud computing(using Amazon's Web Services as our cloud provider).
Appendix, Where to Learn More about Machine Learning, covers a list of wonderfulresources available for machine learning.
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玩于股涨之上 发表于 2019-5-14 22:53:17 |显示全部楼层
There's no greater gift than love.
没有比爱更好的馈赠。
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nkunku 发表于 2019-5-15 08:28:53 |显示全部楼层
Building Machine Learning Systems with Python_Explore Machine Learning and Deep Learning Techniques  Using Scikit-Learn and TensorFlow (3rd Edition)_Luis Pedro Coelho 2018
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307Hotel 发表于 2019-5-15 09:11:22 |显示全部楼层
zhaosl 发表于 2019-5-14 22:09
https://bbs.pinggu.org/thread-6691294-1-1.html

人家第三版才只要10个币哈。
穷的没有币呀  有需求的免费赠与
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aibieli731001 发表于 2019-5-15 11:29:12 |显示全部楼层
好书,多谢
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wyr629 在职认证  发表于 2019-5-15 22:08:47 |显示全部楼层
谢谢分享
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