Troubleshooting Python Machine L

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. What do you do?

Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge.
Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.

All the code and supporting files are available on GitHub at -https://github.com/PacktPublishing/Troubleshooting-Python-Machine-Learning-

Python机器学习排错 你是一名数据科学家。每天你盯着大量的数据试图应用最新、最智能的模型发现找到新的发现，但是看起来这充满了无尽的障碍。你的同事依靠你将企业的数据变现，时间正在一点一滴的流失，你要怎么办呢？ 本教程是你的答案。我们系统地围绕着数据整理、调试诸如随机森林和SVM模型以及对棘手的结果可视化等方面，研究了在网上出现的常见的ML问题。我们利用Stack Overflow、Medium和Github中的统计学，获得了数据科学家努力获取的交叉比较。我们问题的重要性进行了分级，如检索最重要的递归功能和在集群后解释结果以及他们相对应的解决方案。我们以一种“问题-解决方案”的方式呈现出案例研究，使其方便你使用。 学习完本教程，将会帮助你准确地调试模型和研究管道，这样你可以集中于新想法而非修改老问题。

链接: https://pan.baidu.com/s/1dKLZhDvjc7fDTxL1FB6dlw 密码: awyi