- Data Science
- data forecasting
- Machine Learning
- Data Analysis
- Supply Chain
- Pandas
- Numpy
- Linear Programming (LP)
- Python Programming
- Autoregressive Integrated Moving Average (ARIMA)
- Time Series
- Demand Forecasting
Machine Learning for Supply Chains 專項課程
Use Machine Learning in the Supply Chain. You will learn to use machine language techniques to analyze and predict retail stock in the supply chain.
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您將學到的內容有
Learn to merge, clean, and manipulate data using Python libraries such as Numpy and Pandas
Gain familiarity with the basic and advaned Python functonalities such as importing and using modules, list compreohensions, and lambda functions.
Solve a supply chain cost optimization problem using Linear Programming with Pulp
Building ARIMA models in Python to make demand predictions
您將獲得的技能
關於此 專項課程
應用的學習項目
You will learn and practice skills as you go through each of the courses, using the Coursera lab environment. The final course is a capstone project where you will analyze data and make predictions about retail product usage, and then calculate optimal safety stock storage.
There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
專項課程的運作方式
加入課程
Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。
實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。
獲得證書
在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

此專項課程包含 4 門課程
Fundamentals of Machine Learning for Supply Chain
This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.
Demand Forecasting Using Time Series
This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.
Advanced AI Techniques for the Supply Chain
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.
Capstone Project: Predicting Safety Stock
In this course, we'll make predictions on product usage and calculate optimal safety stock storage. We'll start with a time series of shoe sales across multiple stores on three different continents. To begin, we'll look for unique insights and other interesting things we can find in the data by performing groupings and comparing products within each store. Then, we'll use a seasonal autoregressive integrated moving average (SARIMA) model to make predictions on future sales. In addition to making predictions, we'll analyze the provided statistics (such as p-score) to judge the viability of using the SARIMA model to make predictions. Then, we'll tune the hyper-parameters of the model to garner better results and higher statistical significance. Finally, we'll make predictions on safety stock by looking to the data for monthly usage predictions and calculating safety stock from the formula involving lead times.
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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.
常見問題
退款政策是如何规定的?
我可以只注册一门课程吗?
有助学金吗?
我可以免费学习课程吗?
此课程是 100% 在线学习吗?是否需要现场参加课程?
完成专项课程需要多长时间?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
完成专项课程后我会获得大学学分吗?
What will I be able to do upon completing the Specialization?
還有其他問題嗎?請訪問 學生幫助中心。