- 具备机器学习的基本知识，例如监督式学习和非监督式学习
- 熟悉Python编程（变量、循环、函数）
- 有使用pandas或NumPy等库处理数据的经验会有帮助，但不是必须的
- 不需要具备高级建模或神经网络的经验
目标受众
- 数据科学家
- Business分析师
- 处理数据的软件工程师和技术专业人员
Machine Learning 和 Predictive Analytics 与 Python 是一门全面的培训课程，涵盖监督式和非监督式学习技术、模型评估以及使用真实数据的实际应用。
这门由讲师指导的培训（线上或线下）面向中级数据专业人士，旨在帮助他们将机器学习技术应用于数据驱动的业务问题，包括使用神经网络进行销售预测和预测建模。
在本培训结束时，参与者将能够：
- 理解机器学习的核心概念和类型。
- 应用分类、回归、聚类和关联分析的关键算法。
- 使用 Python 进行探索性数据分析和数据准备。
- 使用神经网络进行非线性建模任务。
- 实施预测分析以进行业务预测，包括销售数据。
- 使用视觉和统计技术评估和优化模型性能。
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动手操作。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
Machine Learning 介绍
- 机器学习的类型 – 监督式与非监督式
- 从统计学习到机器学习
- 数据挖掘的工作流程：业务理解、数据准备、建模、部署
- 为任务选择合适的算法
- 过拟合与偏差-方差权衡
Python 和 ML 函式库概述
- 为何使用程式语言进行机器学习
- 在 R 和 Python 之间选择
- Python 速成课程与 Jupyter Notebooks
- Python 函式库：pandas、NumPy、scikit-learn、matplotlib、seaborn
测试与评估机器学习算法
- 泛化、过拟合与模型验证
- 评估策略：保留法、交叉验证、自助法
- 回归的评估指标：ME、MSE、RMSE、MAPE
- 分类的评估指标：准确率、混淆矩阵、不平衡类别
- 模型性能可视化：利润曲线、ROC 曲线、提升曲线
- 模型选择与网格搜索调参
数据准备
- 在 Python 中导入与储存数据
- 探索性分析与摘要统计
- 处理缺失值与异常值
- 标准化、正规化与转换
- 定性数据重新编码与使用 pandas 进行数据整理
分类算法
- 二元分类与多元分类
- 逻辑回归与判别函数
- 朴素贝叶斯、k-近邻
- 决策树：CART、Random Forests、Bagging、Boosting、XGBoost
- 支持向量机与核函数
- 集成学习技术
回归与数值预测
- 最小二乘法与变量选择
- 正则化方法：L1、L2
- 多项式回归与非线性模型
- 回归树与样条
Neural Networks
- 神经网络与深度学习介绍
- 激活函数、层与反向传播
- 多层感知器 (MLP)
- 使用 TensorFlow 或 PyTorch 进行基本神经网络建模
- 神经网络用于分类与回归
销售 Forecasting 与 Predictive Analytics
- 时间序列与基于回归的预测
- 处理季节性与趋势数据
- 使用机器学习技术构建销售预测模型
- 评估预测准确性与不确定性
- Business 结果解释与沟通
Unsupervised Learning
- 聚类技术：k-means、k-medoids、层次聚类、SOMs
- 降维：PCA、因子分析、SVD
- 多维尺度分析
文本挖掘
- 文本预处理与分词
- 词袋模型、词干提取与词形还原
- 情感分析与词频分析
- 使用词云可视化文本数据
推荐系统
- 基于用户与基于项目的协同过滤
- 设计与评估推荐引擎
关联模式挖掘
- 频繁项集与 Apriori 算法
- 市场篮子分析与提升比
异常检测
- 极值分析
- 基于距离与基于密度的方法
- 高维数据中的异常检测
Machine Learning 案例研究
- 理解业务问题
- 数据预处理与特征工程
- 模型选择与参数调优
- 评估与结果展示
- 部署
总结与下一步
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