Course Code: mlbankingpython_
Duration: 21 hours
Prerequisites:
  • Python编程经验
  • 基本熟悉统计学和线性代数
Overview:

受众

  • 开发人员
  • 数据科学家

课程形式

  • 部分讲座、部分讨论、练习和大量实操
Course Outline:

介绍

  • 统计学习（统计分析）和机器学习之间的区别
  • 金融和银行公司采用机器学习技术及招募相关人才

不同类型的机器学习

  • 有监督学习 vs 无监督学习
  • 迭代和评估
  • 偏差方差权衡
  • 结合有监督学习和无监督学习（半监督学习）

机器学习语言和工具集

  • 开源 vs 专有系统和软件
  • Python vs R vs Matlab
  • 库和框架

机器学习案例研究

  • 消费者数据和大数据
  • 评估消费者和商业贷款的风险
  • 通过情感分析改善客户服务
  • 检测身份欺诈、帐单欺诈和洗钱

实践：用于机器学习的Python

  • 准备开发环境
  • 获取Python机器学习库和包
  • 使用scikit-learn和PyBrain

如何加载机器学习数据

  • 数据库、数据仓库和流数据
  • 使用Hadoop和Spark进行分布式存储和处理
  • 导出的数据和Excel

在有监督学习的情况下对业务决策进行建模

  • 对您的数据进行分类（分类）
  • 使用回归分析来预测结果
  • 从可用的机器学习算法中选择
  • 理解决策树算法
  • 理解随机森林算法
  • 模型评估
  • 练习

回归分析

  • 线性回归
  • 概括和非线性
  • 练习

分类

  • Bayesian refresher
  • 朴素贝叶斯（Naive Bayes）方法
  • 逻辑回归
  • k最近邻算法
  • 练习

实践：建立一个估计模型

  • 根据客户类型和历史来评估贷款风险

评估机器学习算法的性能

  • 交叉验证和重采样
  • Bootstrap aggregation (bagging)
  • 练习

在无监督学习的情况下对业务决策进行建模

  • 样本数据集不可用时
  • K均值聚类
  • 无监督学习的挑战
  • 超越均值（K-means）
  • 贝叶斯（Bayes）网络和马尔可夫（Markov）隐藏模型
  • 练习

实践：建立一个推荐系统

  • 分析过去的客户行为以改进新的服务产品

扩展您公司的能力

  • 在云中开发模型
  • 借助GPU加速机器学习
  • 运用深度学习神经网络进行计算机视觉、语音识别和文本分析

结束语

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