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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-powered-demand-forecasting/
课程评论：没有评论
课程名称：人工智能驱动的需求预测 概述：在当今节奏快速的供应链中，准确的需求预测至关重要，而人工智能正在彻底改变企业预测需求的方式。本课程旨在为计划者、分析师和供应链专业人士提供将人工智能整合到预测过程中的技能。 课程内容包括：首先，您将学习需求预测的基本原理，探讨传统技术（如移动平均法、指数平滑法、线性回归和ARIMA）以及针对间歇性需求的专门方法，如克罗斯顿方法。课程将深入讲解如何以及何时应用每个模型，使用真实商业案例和直观的解释。 接下来，我们将进入人工智能和机器学习的世界。您将发现人工智能如何提升预测的准确性，处理大数据集，适应变化的趋势，并揭示传统模型可能忽略的模式。通过实际演示和动手指导，您将学习使用基于Python的工具和平台，如Prophet、Scikit-learn，以及基于云的解决方案，如AWS Forecast和Google AutoML。 无论您是管理库存、规划生产，还是领导需求规划的倡议，本课程将帮助您超越电子表格，迈向基于数据的智能预测。不需要之前的编码经验，只需好奇心、分析思维以及将预测引入数字化转型未来的愿望。 讲师：Abraham Natanael Camargo Ortega
In today's fast-paced supply chains, accurate demand forecasting is critical-and Artificial Intelligence is revolutionizing how businesses predict demand. This course is designed to equip planners, analysts, and supply chain professionals with the skills to integrate AI into their forecasting processes.You'll start by learning the fundamentals of demand forecasting, exploring both traditional techniques (like Moving Average, Exponential Smoothing, Linear Regression, and ARIMA) and specialized methods for intermittent demand such as Croston's Method. We'll dive deep into how and when to apply each model, using real-world business examples and intuitive explanations.Next, we shift into the world of AI and machine learning. You'll discover how AI enhances forecasting accuracy, handles large datasets, adapts to changing trends, and uncovers patterns that traditional models might miss. With practical demos and hands-on guidance, you'll learn to use Python-based tools and platforms like Prophet, Scikit-learn, and even cloud-based solutions such as AWS Forecast and Google AutoML.Whether you're managing inventory, planning production, or leading demand planning initiatives, this course will help you move beyond spreadsheets and into data-driven, intelligent forecasting. No previous coding experience is required-just curiosity, analytical thinking, and a desire to bring forecasting into the future of digital transformation.Instructor: Abraham Natanael Camargo Ortega
