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
Ce cours fait partie de Spécialisation Machine Learning for Supply Chains
Instructeurs : Rajvir Dua
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Compétences que vous acquerrez
- Catégorie : Bias–Variance Tradeoff
- Catégorie : Machine Learning
- Catégorie : Supply Chain
- Catégorie : Natural Language Processing
- Catégorie : Image Analysis
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Il y a 4 modules dans ce cours
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.
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4 vidéos4 lectures2 devoirs1 sujet de discussion1 laboratoire non noté
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.
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3 vidéos4 lectures1 devoir1 devoir de programmation3 laboratoires non notés
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.
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5 vidéos5 lectures1 devoir1 sujet de discussion2 laboratoires non notés
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.
Inclus
1 devoir de programmation1 laboratoire non noté
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Recommandé si vous êtes intéressé(e) par Machine Learning
University of Virginia
Fundação Instituto de Administração
Macquarie University
Amazon Web Services
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