In this course, we’ll learn about more advanced machine learning methods that are used to tackle problems in the supply chain. We’ll start with an overview of the different ML paradigms (regression/classification) and where the latest models fit into these breakdowns. Then, we’ll dive deeper into some of the specific techniques and use cases such as using neural networks to predict product demand and random forests to classify products. An important part to using these models is understanding their assumptions and required preprocessing steps. We’ll end with a project incorporating advanced techniques with an image classification problem to find faulty products coming out of a machine.
Advanced AI Techniques for the Supply Chain
Advanced AI Techniques for the Supply Chain
本课程是 Machine Learning for Supply Chains 专项课程 的一部分
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您将获得的技能
- Model Evaluation
- Deep Learning
- Artificial Neural Networks
- Natural Language Processing
- Supply Chain
- Convolutional Neural Networks
- Machine Learning
- Forecasting
- Unsupervised Learning
- Supervised Learning
- Customer Demand Planning
- Image Analysis
- Random Forest Algorithm
- Applied Machine Learning
- Classification And Regression Tree (CART)
- Anomaly Detection
- 技能部分已折叠。显示 9 项技能，共 16 项。
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作业
4 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Machine Learning for Supply Chains 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有4个模块
In this module, we'll learn about the use cases of machine learning in the supply chain. We'll start with the big picture applications before diving deeper into specific algorithms, including neural networks. Throughout the module, we'll explain not only the general artificial intelligence concepts and mathematics, but also how these algorithms can specifically be used for the supply chain.
涵盖的内容
4个视频4篇阅读材料2个作业1个讨论话题1个非评分实验室
In this module, we'll cover the concepts relating to the ML paradigm. We'll start by learning how to pick a model, relying on considerations such as managing the bias-variance tradeoff. Next, we'll explore how machine learning models converge, including the use of stochastic gradient descent to minimize loss functions. Finally, we'll end with some practical considerations on coding advanced AI models with libraries for hyperparamter tuning.
涵盖的内容
3个视频4篇阅读材料1个作业1个编程作业3个非评分实验室
In this module, we'll expand beyond numbers and learn how to use machine learning on images and text. We'll start by talking about how to analyze text data and cover the primary methods behind natural language processing. Then, we'll learn how to analyze images by constructing convolutional neural networks complete with convolutions and pooling layers.
涵盖的内容
5个视频5篇阅读材料1个作业1个讨论话题2个非评分实验室
In this final project, we’ll apply what we learned in the last module to classify images of products based on whether there is a defect or not.
涵盖的内容
1个编程作业1个非评分实验室
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