Course Code: mlbusai
Duration: 21 hours
Prerequisites:
  • 对机器学习原理及其应用的基本理解
  • 熟悉在试算表环境或数据分析工具中的操作
  • 接触过Python或其他程式语言会有帮助,但不是必须的
  • 有兴趣将机器学习应用于实际业务和预测问题

目标受众

  • Business分析师
  • AI专业人士
  • 数据驱动的决策者和经理
Overview:

Machine Learning for Business 和 AI 应用是一门实践课程,旨在为专业人士提供理解和实践技能,以将机器学习技术应用于现实世界的业务挑战,包括销售预测和基于AI系统的决策支持。

这项由讲师主导的培训(线上或线下)针对中级业务和AI专业人士,他们希望通过实际案例和基于Python的工具,在业务、预测和AI驱动系统中应用机器学习。

在培训结束时,参与者将能够:

  • 理解机器学习如何融入AI和业务策略。
  • 将监督和非监督学习技术应用于结构化业务问题。
  • 对数据进行预处理和转换以进行建模。
  • 使用神经网络进行分类和预测任务。
  • 使用统计和基于机器学习的方法进行销售预测。
  • 实施聚类和关联规则挖掘以进行客户细分和模式发现。

课程形式

  • 互动式讲座和讨论。
  • 大量练习和实践。
  • 在实时实验室环境中进行实践操作。

课程定制选项

  • 如需为此课程定制培训,请联系我们安排。
Course Outline:

Machine Learning 在 Business 中的介绍

  • 机器学习作为人工智能的核心组成部分
  • 机器学习的类型:监督学习、无监督学习、强化学习、半监督学习
  • 商业应用中常见的机器学习算法
  • 机器学习在人工智能中的挑战、风险和潜在用途
  • 过拟合与偏差-方差权衡

Machine Learning 技术与工作流程

  • Machine Learning 的生命周期:从问题到部署
  • 分类、回归、聚类、异常检测
  • 何时使用监督学习与无监督学习
  • 理解强化学习在业务自动化中的应用
  • 机器学习驱动决策的考量

数据预处理与特征工程

  • 数据准备:加载、清理、转换
  • 特征工程:编码、转换、创建
  • 特征缩放:标准化、归一化
  • 降维:主成分分析、变量选择
  • 探索性数据分析与业务数据可视化

Neural Networks 与 Deep Learning

  • 神经网络及其在商业中的应用介绍
  • 结构:输入层、隐藏层、输出层
  • 反向传播与激活函数
  • 用于分类与回归的神经网络
  • 神经网络在预测与模式识别中的应用

销售 Forecasting 与 Predictive Analytics

  • 时间序列与基于回归的预测
  • 时间序列分解:趋势、季节性、周期
  • 技术:线性回归、指数平滑、ARIMA
  • 用于非线性预测的神经网络
  • 案例研究:Forecasting 月销售量

Business 应用案例研究

  • 使用线性回归进行改进预测的高级特征工程
  • 使用聚类与自组织映射进行细分分析
  • 市场篮子分析与关联规则挖掘以获取零售洞察
  • 使用逻辑回归、决策树、XGBoost、SVM 进行客户违约分类

总结与下一步

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