使用机器学习的信用卡欺诈检测 信用卡欺诈是一个日益严重的问题，面临许多挑战，包括时间漂移和严重的阶级失衡。 该项目尝试使用包括自适应合成采样方法（ADASYN）和合成少数采样率（SMOTE）在内的最新技术来解决班级不平衡问题。 2013年9月在欧洲进行的超过280k真实交易[1]被用作训练数据集。 比较了三种类型的机器学习模型：随机森林，支持向量机和多层感知器。 结果表明，不平衡数据集的最佳采样方法取决于数据集和所使用的模型。 该项目包含以下组件： a）PDF格式的IEEE风格论文 b）Jupyter Notebook进行了机器学习测试。 您可以运行视图并自己运行它们。 还包括注释，推理和数字。 为了方便起见，我在此git repo中包含了原始数据集的副本[1]，但是请参考原始资源以获取最新版本。 该项目是2017年冬季在滑铁卢大学进行的SYDE 522：机器学习的一部分。 安装 克隆项目： $ git clone https://github.com/yazanobeidi/fraud-detection.git && cd fraud-detection Pip安装依赖项

乳腺癌预测：乳腺癌分析

Python Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners)

(英文第二版)Mastering Machine Learning with scikit-learn, 2nd Edition(英文第二版)Mastering Machine Learning with scikit-learn, 2nd Edition

Mastering Machine Learning with scikit-learn - Second Edition by Gavin Hackeling English | 24 July 2017 | ASIN: B06ZYRPFMZ | ISBN: 1783988363 | 254 Pages | AZW3 | 5.17 MB Key Features Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Book Description Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. What you will learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the per

gplearn：Python中的遗传编程，具有受scikit-learn启发的API

ml-forex-prediction：使用机器学习预测外汇未来价格

Ray Core导览 关于利用核心功能实现分布式模式的入门教程。 注意：这些示例已在以下位置使用Python 3.7+进行了测试： Ubuntu 18.04 LTS macOS 10.13 随附的幻灯片位于： : 入门 首先，使用git克隆此公共存储库： git clone https://github.com/DerwenAI/ray_tutorial.git cd ray_tutorial 设置本地并激活它： python3 -m venv venv source venv/bin/activate 然后使用pip安装所需的依赖项： pip install -U pip pip install -r requirements.txt 或者，如果您使用conda来安装Python软件包： conda create -n ray_tutorial python=3.7

需要的题量多的话建议购买付费专栏（包含上百道题目答案，并持续更新中），性价比更高。

numpy，pandas，scipy，scikit-learn等教程和源码，适合新手入门使用，非常全面！