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所在平台: Udemy |
课程主页: https://www.udemy.com/course/industry-40-digital-transformation-and-smart-manufacturing/
课程评论：没有评论
**课程名称：** 工业4.0 机器学习项目 **课程概述：** 本课程“工业4.0 机器学习项目”侧重于在各行各业和不同领域进行实际、动手操作的项目。旨在为学员提供在营销、工程、金融和预测等多元化领域应用数据科学技术的真实世界经验。 **课程内容：** * **项目实践：** 学员将完成一系列真实项目，涵盖数据分析、预测建模、时间序列预测、异常检测等。 * **技术应用：** 学习应用ARIMA、LSTM、随机森林、梯度提升和聚类方法等流行算法进行机器学习和数据科学。 * **特征工程：** 利用SHAP和Boruta等工具进行特征选择和工程，并学习构建高效的数据管道。 * **案例研究：** 解决客户流失预测、信用卡欺诈检测、销售预测、员工流失分析和传感器数据建模等实际场景问题。 * **循序渐进：** 每个项目都以分步方式呈现，帮助学员理解通过数据科学解决业务问题的研究方法。 **课程目标：** 通过真实数据集和广泛的主题，提升学员的实践技能，以满足不同兴趣和职业道路的需求。 **适合人群：** 具有基础编程和数据科学知识，希望通过多样化项目实践来提升技能的学习者。无论是希望转型数据科学领域，还是想通过实际应用加深经验，本课程都将帮助您构建强大的项目集。
Welcome to "Machine Learning Projects for Industry 4.0," a comprehensive course focused on practical, hands-on projects across a wide range of industries and domains. This course is designed to provide real-world experience in applying data science techniques to diverse fields such as marketing, engineering, finance, and forecasting.In this course, you will:Work on a variety of real-world projects involving data analysis, predictive modeling, time series forecasting, anomaly detection, and more.Apply machine learning and data science techniques using popular algorithms like ARIMA, LSTM, Random Forest, Gradient Boosting, and clustering methods.Practice feature selection and engineering using tools like SHAP and Boruta, and learn how to build effective data pipelines.Tackle practical scenarios, from customer churn prediction and credit card fraud detection to sales forecasting, employee turnover analysis, and sensor data modeling.Each project is presented with a step-by-step approach to help you understand the methodology behind solving business problems using data science. The course aims to build your practical skills by focusing on real-life datasets and covering a broad range of topics to cater to different interests and career paths.This course is ideal for learners with a basic understanding of programming and data science who wish to enhance their skills by working on a diverse set of projects. Whether you are looking to transition into data science or to deepen your experience through hands-on applications, this course will help you build a strong project portfolio.
