Machine Learning
本课程系统介绍机器学习算法及其商业应用。内容涵盖监督学习（线性回归、逻辑回归、决策树、随机森林、SVM、KNN）、无监督学习（K-Means聚类、层次聚类、主成分分析）、模型评估与选择、交叉验证、特征工程、过拟合与正则化等。课程使用scikit-learn库进行实践，结合客户分群、信用评分、推荐系统、销量预测等商业场景，培养学生运用机器学习解决实际商业问题的能力。
This course systematically introduces machine learning algorithms and their business applications. Topics include supervised learning (linear regression, logistic regression, decision trees, random forests, SVM, KNN), unsupervised learning (K-Means, hierarchical clustering, PCA), model evaluation and selection, cross-validation, feature engineering, overfitting, and regularization. Using scikit-learn with business scenarios like customer segmentation, credit scoring, recommendation systems, and sales forecasting, students develop skills to solve real business problems with machine learning.
基于真实房屋交易数据集，完成特征工程、模型选择、调参与评估，对比多种算法表现，最终部署预测模型并撰写技术报告，分析模型可解释性。
Based on a real housing transaction dataset, complete feature engineering, model selection, tuning, and evaluation, compare multiple algorithms, deploy the prediction model, and write a technical report analyzing model interpretability.