吴恩达老师机器学习课的笔记。黄海广博士原创，黄海广博士也是这门课的最初翻译者。包括课堂笔记，数学基础，Python代码。

非常不错的资源，内容详实丰富;daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

英文。2018版本。The authors of this book have leveraged their hands-on experience with solving real-world problems using Python and its Machine Learning ecosystem to help the readers gain the solid knowledge needed to apply essential concepts, methodologies, tools, and techniques for solving their own real-world problems and use-cases. Practical Machine Learning with Python aims to cater to readers with varying skill levels ranging from beginners to experts and enable them in structuring and building practical Machine Learning solutions.

小甲鱼零基础入门学习python课后习题与答案 小甲鱼零基础入门学习python课后习题与答案

最新版非常清晰的彩色的pdf + 源代码，作者通过具体的例子，应用两款非常流行的Python框架：Scikit-Learn和TensorFlow，帮助你掌握构建机器学习系统所需要的概念和工具。

Cats vs, Dogs 的python原创代码， ipynb格式。图片数据来源https://www.kaggle.com/c/dogs-vs-cats 任务如下： Task: In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult. Data Description: The training archive contains 25,000 images of dogs and cats. Train your algorithm on these files and predict the labels for test1.zip (1 = dog, 0 = cat). Submission Format Your submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat): id,label 1,0 2,0 3,0 etc...

这个资源是包含python的PPT资源，快来加入机器学习吧。

利用python处理UCI鲍鱼年龄预测数据，运用了经典回归、决策树、随机森林、SVM等十余种机器学习方法，附有数据集以及详细python代码

本资源包内是150行鸢尾花数据集，用于Python建模学习，适用于初学者使用

Francois Chollet-Deep Learning with Python，Francois Chollet-Deep Learning with Python