|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/supervised-machine-learning-in-python/
课程评论：没有评论
**Coursera课程总结：Python中的监督式机器学习** 本课程是一门实践性课程，专注于监督式机器学习及其在Python编程语言中的应用。 **核心内容：** * **监督式机器学习基础：** 了解监督式机器学习的定义、目标是构建预测模型。 * **模型优化与数据洞察：** 学习如何通过优化模型来提取数据中的隐藏信息，并用于推断和预测。 * **特征重要性：** 掌握特征重要性的计算方法，以理解数据背后的信息，并通过丢弃无关变量来降低问题维度。重点介绍SHAP技术。 * **超参数调优：** 学习利用交叉验证技术对模型进行超参数调优。 **课程将教授以下模型和技术：** * **模型类型：** 回归模型、分类模型 * **线性模型：** 线性回归、Lasso回归、Ridge回归、Elastic Net回归、逻辑回归 * **其他模型：** 决策树、朴素贝叶斯、K近邻、支持向量机（线性与非线性）、前馈神经网络 * **集成模型：** Bagging、随机森林、Boosting、梯度提升、Voting、Stacking * **核心概念：** 偏差-方差权衡（Bias-variance tradeoff） **性能评估：** * **回归指标：** 均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE） * **分类指标：** 混淆矩阵、准确率、平衡准确率、精确率-召回率曲线（Precision-Recall Curve）、ROC曲线及AUC、多类别指标 **特征工程与维度约减：** * **特征重要性计算** * **SHAP技术** * **递归特征消除（Recursive Feature Elimination）** **超参数调优技术：** * **K折交叉验证（k-fold cross-validation）** * **网格搜索（Grid search）** * **随机搜索（Random search）** **学习环境与工具：** * **编程语言：** Python * **核心库：** scikit-learn * **开发环境：** Jupyter Notebook（提供可下载的Notebook文件） 课程将以理论介绍和Python实际操作相结合的方式进行，通过scikit-learn库和Jupyter环境，帮助学习者掌握监督式机器学习的理论和实践技能。
In this practical course, we are going to focus on supervised machine learning and how to apply it in Python programming language.Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive models starting from a dataset. With the proper optimization of the models, it is possible to create mathematical representations of our data in order to extract the information that is hidden inside our database and use it for making inferences and predictions.A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAP technique.Finally, the proper optimization of a model is possible using some hyperparameter tuning techniques that make use of cross-validation.With this course, you are going to learn:What supervised machine learning isWhat overfitting and underfitting are and how to avoid themThe difference between regression and classification modelsLinear modelsLinear regressionLasso regressionRidge regressionElastic Net regressionLogistic regressionDecision treesNaive BayesK-nearest neighborsSupport Vector MachinesLinear SVMNon-linear SVMFeedforward neural networksEnsemble modelsBias-variance tradeoffBagging and Random ForestBoosting and Gradient BoostingVotingStackingPerformance metricsRegressionRoot Mean Squared ErrorMean Absolute ErrorMean Absolute Percentage ErrorClassificationConfusion matrixAccuracy and balanced accuracyPrecisionRecallROC Curve and the area under itMulti-class metricsFeature importanceHow to calculate feature importance according to a modelSHAP technique for calculating feature importance according to every modelRecursive Feature Elimination for dimensionality reductionHyperparameter tuningk-fold cross-validationGrid searchRandom searchAll the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.
