|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/xgboost-machine-learning-for-data-science-and-kaggle/
课程评论：没有评论
课程名称：XGBoost机器学习与数据科学及Kaggle比赛 课程概述：未来的世界是机器学习的AI时代，因此掌握机器学习的应用相当于获得了未来职业的钥匙。如果现在只能学习一个机器学习工具或算法来构建预测模型，那毫无疑问就是XGBoost！若您要参与Kaggle比赛，那么您的首选建模工具也是XGBoost！这一点得到了无数经验丰富的数据科学家和新手的验证。因此，您一定要注册这门课程！ XGBoost之所以在Kaggle比赛中广受欢迎，是因为它具有出色的准确性、速度和稳定性。例如，根据调查，超过70%的Kaggle顶尖获胜者表示他们使用过XGBoost。XGBoost在数据科学世界中非常实用，能够预测多种类型的目标，包括连续数据、二元数据和分类数据，且在解决多类或多标签分类问题上也非常有效。此外，Kaggle平台上的竞赛涵盖了几乎所有行业的应用，如零售、银行、保险、制药研究、交通控制和信用风险管理。 尽管XGBoost功能强大，但在没有专家指导的情况下，充分发挥其潜能并不容易。例如，要成功实现XGBoost算法，您需要理解并调整多个参数设置。为了实现这一点，我将教您基础算法，使您能够根据不同的数据和应用场景配置XGBoost。此外，我还将提供关于特征工程、特征选择和参数调整的 intensive 讲座，旨在使您能够准备适合喂入XGBoost模型的数据或特征。 本课程既实用又不缺乏理论，我们将从决策树及其相关概念和组成部分开始，逐步构建梯度提升方法，最终引导到XGBoost建模。在所有机器学习方法中，数学和统计学知识都会适度应用来解释机制。我们使用Python的pandas数据框进行数据探索和清理。本课程的一个显著特点是，我们通过多个Python程序示例演示每一个知识点和技能，确保您在课程中学习到的内容能够得到实际应用。
The future world is the AI era of machine learning, so mastering the application of machine learning is equivalent to getting a key to the future career. If you can only learn one tool or algorithm for machine learning or building predictive models now, what is this tool? Without a doubt, that is Xgboost! If you are going to participate in a Kaggle contest, what is your preferred modeling tool? Again, the answer is Xgboost! This is proven by countless experienced data scientists and new comers. Therefore, you must register for this course!The Xgboost is so famous in Kaggle contests because of its excellent accuracy, speed and stability. For example, according to the survey, more than 70% the top kaggle winners said they have used XGBoost.The Xgboost is really useful and performs manifold functionalities in the data science world; this powerful algorithm is so frequently utilized to predict various types of targets - continuous, binary, categorical data, it is also found Xgboost very effective to solve different multiclass or multilabel classification problems. In addition, the contests on Kaggle platform covered almost all the applications and industries in the world, such as retail business, banking, insurance, pharmaceutical research, traffic control and credit risk management.The Xgboost is powerful, but it is not that easy to exercise it full capabilities without expert's guidance. For example, to successfully implement the Xgboost algorithm, you also need to understand and adjust many parameter settings. For doing so, I will teach you the underlying algorithm so you are able to configure the Xgboost that tailor to different data and application scenarios. In addition, I will provide intensive lectures on feature engineering, feature selection and parameters tuning aiming at Xgboost. So, after training you should also be able to prepare the suitable data or features that can well feed the XGBoost model.This course is really practical but not lacking in theory; we start from decision trees and its related concepts and components, transferring to constructing the gradient boot methods, then leading to the Xgboost modeling. The math and statistics are mildly applied to explain the mechanisms in all machine learning methods. We use the Python pandas data frames to deal with data exploration and cleaning. One significant feature of this course is that we have used many Python program examples to demonstrate every single knowledge point and skill you have learned in the lecture.
