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.
제공자:
Machine Learning for Supply Chains 특화 과정LearnQuest이 강좌에 대하여
최근 조회 1,625회
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
중급 단계
It is reccomended that you finish the first two courses in the speciliation before trying this one.
완료하는 데 약 22시간 필요
영어
자막: 영어
귀하가 습득할 기술
- Natural Language Processing
- Machine Learning
- Bias–Variance Tradeoff
- Supply Chain
- Image Analysis
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
중급 단계
It is reccomended that you finish the first two courses in the speciliation before trying this one.
완료하는 데 약 22시간 필요
영어
자막: 영어
제공자:
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
Machine Learning in the Supply Chain
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.
완료하는 데 2시간 필요
4개 동영상 (총 11분), 4 개의 읽기 자료, 2 개의 테스트
완료하는 데 14시간 필요
A Classical AI Approach
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.
완료하는 데 14시간 필요
3개 동영상 (총 9분), 4 개의 읽기 자료, 2 개의 테스트
완료하는 데 3시간 필요
Images and Text
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.
완료하는 데 3시간 필요
5개 동영상 (총 19분), 5 개의 읽기 자료, 1 개의 테스트
완료하는 데 4시간 필요
Final Project: Detecting Anomalies with Image Classification
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.
완료하는 데 4시간 필요
Machine Learning for Supply Chains 특화 과정 정보
This specialization is intended for students who wish to use machine language to analyze and predict product usage and other similar tasks. There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
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