- 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
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。
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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