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PYTHON易错题；二级PYTHON易错题；PYTHON易错题；PYTHON易错题(PYTHON is prone to errors, PYTHON is prone to errors , PYTHON is prone to errors,PYTHON is prone to errors.)

说明：  网页设计框架，很实用，能帮助大家迅速开发出大型网站(Web Design Framework)

说明：  DBSCAN（Density-Based Spatial Clustering of Applications with Noise） 为一种基于密度的聚类算法，它不仅可以找出具有任何形状的簇，而且还可以用于检测离群值。其基本思想为数据点分布紧凑的应被划分为一类，而周围未分布有或仅有极少数点的数据点则有可能为离群值。通过python实现了该聚类方法的计算。(DBSCAN (density based spatial clustering of applications with noise) is a density based clustering algorithm. It can not only find clusters with any shape, but also detect outliers. The basic idea is that the data points with compact distribution should be divided into a class, while the data points with few or no distribution around may be outliers. This clustering method is implemented by python.)

廖雪峰的JavaScript python git，非常经典(Liao Xuefeng JavaScript python git)

说明：  用户注册登录，爬虫，echarts数据可视化(User registration, crawler, echarts data visualization)

Python数据挖掘入门与实践 本书作为数据挖掘入门读物，介绍了数据挖掘的基础知识、基本工具和实践方法，通过循序渐进地讲解算法，带你轻松踏上数据挖掘之旅。本书采用理论与实践相结合的方式，呈现了如何使用决策树和随机森林算法预测美国职业篮球联赛比赛结果，如何使用亲和性分析方法推荐电影，如何使用朴素贝叶斯算法进行社会媒体挖掘，等等。本书也涉及神经网络、深度学习、大数据处理等内容。本书面向愿意学习和尝试数据挖掘的程序员。(Introduction and practice of Python data mining As a primer on data mining, this book introduces the basic knowledge, basic tools and practical methods of data mining. By step-by-step explanation of the algorithm, you can easily embark on the journey of data mining. This book uses a combination of theory and practice, showing how to use the decision tree and random forest algorithm to predict the results of the American professional basketball league game, how to use the affinity analysis method to recommend movies, how to use the naive Bayesian algorithm for social media mining ,and many more. This book also covers neural networks, deep learning, big data processing and more. This book is for programmers who are willing to learn and experiment with data mining.)

自已写的图形项界面， 主要功能就是在一个view中，显示item，并可对item进行操作，还有些缩放平移功能没有实现，没有动画效果。

说明：  基于遗传算法选参数的SVM算法 该算法用于预测股价 （小作坊制作的遗传算法233）(A SVM algorithm based on genetic algorithm to predict stock price)

说明：  用jupyter notebook 实现深度学习LSTM单变量多步的时间序列预测(Using jupyter notebook to realize multi-step time series prediction of deep learning LSTM)

基于Python语言和OpenCV实现了笑脸检测，首先进行人脸检测，然后在人脸检测的基础上进行笑脸检测。(This code implement smile detection based on OpenCV and Python. Face detection is done firstly and then smile detection is done based on the output face of the face detection.)