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所在平台: Udemy |
课程主页: https://www.udemy.com/course/decision-trees-random-forests-get-ready-with-python/
课程评论：没有评论
Coursera 课程总结：使用 Scikit-learn 精通决策树和随机森林 本课程旨在帮助您掌握在数据分析项目中使用决策树和随机森林的技能，以解决分类和回归问题。课程重点关注分类问题，因为大多数成功的机器学习应用都集中于此。 **课程内容涵盖：** * **决策树基础：** 学习决策树在分类和回归问题中的应用，以及构建决策树的关键要素。 * **Scikit-learn 应用：** 掌握使用 `sklearn` 库中的参数来定义决策树分类器 (`DecisionTreeClassifier`) 和回归器 (`DecisionTreeRegressor`)。 * **决策树预测：** 学习如何使用 Scikit-learn 进行决策树的预测，包括模型拟合、剪枝/调优以及结果分析。 * **随机森林原理：** 深入理解随机森林算法的预测原理。 * **Scikit-learn 随机森林：** 掌握使用 `sklearn` 库中的参数来定义随机森林分类器 (`RandomForestClassifier`) 和回归器 (`RandomForestRegressor`)。 * **随机森林预测：** 学习如何使用 Scikit-learn 进行随机森林的预测，包括模型拟合、调优和分析。 * **模型特性：** 理解已拟合的决策树和随机森林的特性，以及数据对模型性能的重要性。 * **预测项目实践：** 指导您如何通过实际项目来应用决策树和随机森林进行预测。 **课程特色：** * **实用性强：** 课程为希望使用 Scikit-learn 进行预测的学生提供实践经验，配备了超过 50 个 Jupyter Notebooks 和相应的 Python 代码，供您学习和练习。 * **内容丰富：** 每个视频课程都包含决策树或随机森林的讲解，并结合 Jupyter Notebooks 中的示例进行演示。 * **学习资源：** 您可以下载 Notebooks 进行回顾、尝试不同的模型参数或数据进行更深入的练习。 * **学生评价：** 学生普遍认为课程信息有价值、讲解清晰、讲师知识渊博，并且练习活动很有帮助。 本课程将帮助您有效应用决策树和随机森林进行预测，并深入理解数据集的预测结构。
The lessons of this course help you mastering the use of decision trees and random forests for your data analysis projects. You will learn how to address classification and regression problems with decision trees and random forests. The course focuses on decision tree classifiers and random forest classifiers because most of the successful machine learning applications appear to be classification problems. The lessons explain:Decision trees for classification and regression problems.Elements of growing decision trees.The sklearn parameters to define decision tree classifiers and regressors.Prediction with decision trees using Scikit-learn (fitting, pruning/tuning, investigating).The sklearn parameters to define random forest classifiers and regressors.Prediction with random forests using Scikit-learn (fitting, tuning, investigating).The ideas behind random forests for prediction.Characteristics of fitted decision trees and random forests.Importance of data and understanding prediction performance.How you can carry out a prediction project using decision trees and random forests.Focusing on classification problems, the course uses the DecisionTreeClassifier and RandomForestClassifier methods of Python's Scikit-learn library to explain all the details you need for understanding decision trees and random forests. It also explains and demonstrates Scikit-learn's DecisionTreeRegressor and RandomForestRegressor methods to adress regression problems. It prepares you for using decision trees and random forests to make predictions and understanding the predictive structure of data sets.This is what is inside the lessons:This course is for people who want to use decision trees or random forests for prediction with Scikit-learn. This requires practical experience and the course facilitates you with Jupyter notebooks to review and practice the lessons' topics.Each lesson is a short video to watch. Most of the lessons explain something about decision trees or random forests with an example in a Jupyter notebook. The course materials include more than 50 Jupyter notebooks and the corresponding Python code. You can download the notebooks of the lessons for review. You can also use the notebooks to try other definitions of decision trees and random forests or other data for further practice.What students commented on this course:Valuable information.Clear explanations.Knowledgeable instructor.Helpful practice activities.
