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所在平台: Udemy |
课程主页: https://www.udemy.com/course/xgboost-deep-dive-hands-on-machine-learning-data-science/
课程评论：没有评论
课程名称：深入探讨XGBoost：Python与Pandas实用数据科学 概述：深入探讨XGBoost课程是一个全面的项目，教授学生成为Python机器学习工程师或数据科学家所需的关键技能。该课程重点讲解Python版本的XGBoost，这是一种针对表格数据的最佳及最流行的算法，并教会学生如何有效地将其应用于各种机器学习任务。在课程中，学生将学习如何利用Pandas进行特征工程和数据可视化，并理解如何定义一个机器学习项目，从原始数据到训练模型的整个过程。学生还将学习梯度提升决策树，并利用真实数据集和实践项目来应用知识。 此外，学生将学习如何将XGBoost应用于自然语言处理（NLP）问题，结合深度学习（句子变换器）和TF-IDF特征进行分析。课程包括五个实践项目： 1. 监督回归问题：预测Airbnb房源价格。 2. 二分类问题：处理网站访问实际日志以预测在线转化。 3. 多分类问题：预测客户的信用评级分为三类。 4. 多类文本分类问题：处理大型数据集和超过200个类别的分类。 5. 时间序列预测问题：使用XGBoost进行预测。 课程结束时，学生将对如何使用XGBoost、Pandas和Python有深入的理解，并能够将这些技能应用于自己的机器学习和数据科学项目中。
The XGBoost Deep Dive course is a comprehensive program that teaches students the top skills they need to become a Python machine learning engineer or data scientist. The course focuses on using the Python version of XGBoost, the best and most popular algorithm for tabular data, and teaches students how to use it effectively for a variety of machine learning tasks.Throughout the course, students will learn how to leverage Pandas for feature engineering and data visualization, and will understand how to define a machine learning project, going from raw data to a trained model. They will also learn about gradient boosting decision trees and will work with realistic datasets and hands-on projects to apply their knowledge in a practical setting.In addition, students will learn how to apply XGBoost to Natural Language Processing (NLP) problems using deep learning (Sentence Transformers) and TF-IDF features.The course includes five hands-on projects with Python:A supervised regression problem where students predict Airbnb listing prices.A binary classification problem where students work with actual logs of website visits to predict online conversions.A multi-class classification problem where we would predict the credit rating of customers in 3 categoriesA multi-class text classification problem where students work with large datasets and more than 200 classes.A time series forecasting problem where students use XGBoost to make predictions.By the end of the course, students will have a strong understanding of how to use XGBoost, Pandas and Python and will be able to apply these skills to their own machine learning and data science projects.
