This specialization course, offered by LearnQuest, focuses on utilizing machine learning in supply chains. It is designed for students interested in using machine learning to analyze and predict product usage and similar tasks within supply chains. No specific prerequisites are required, but a general understanding of supply chain, as well as basic knowledge of statistics and calculus, will be beneficial.
Throughout the course, students will cover various modules that include learning the fundamentals of machine learning for supply chain, demand forecasting using time series, advanced AI techniques for the supply chain, and a capstone project on predicting safety stock. The modules are designed to provide hands-on experience in using Python libraries, building ARIMA models, understanding advanced neural networks, and applying more advanced machine learning methods for supply chain problems.
By the end of the course, students will be equipped with the knowledge and skills necessary to use machine learning techniques effectively in supply chain management and decision-making processes.
Certificate Available ✔
Get Started / More Info
The course modules cover fundamental machine learning techniques using Python libraries, demand forecasting with time series, advanced AI applications in supply chains, and a capstone project on safety stock prediction.
Learn the fundamentals of machine learning for supply chain, including merging, cleaning, and manipulating data using Python libraries such as Numpy and Pandas. Gain familiarity with basic and advanced Python functionalities, and solve a supply chain cost optimization problem using Linear Programming with Pulp.
Gain expertise in demand forecasting by building ARIMA models in Python to make accurate predictions. Develop an understanding of autocorrelation and autoregressive models to lay the framework for advanced neural networks such as LSTMs.
Explore advanced machine learning methods for supply chain problems, including regression, classification, and specific techniques such as using neural networks to predict product demand and random forests to classify products. Understand their assumptions and required preprocessing steps and work on a project incorporating advanced techniques with an image classification problem to find faulty products.
Complete a capstone project by calculating safety stock using SARIMA predictions combined with manipulating lead times, applying the knowledge and skills acquired throughout the course to a real-world scenario.
Convolutional Neural Networks is a comprehensive course that delves into computer vision, enabling learners to build and apply convolutional neural networks, understand...
Identify Damaged Car Parts with Vertex AutoML Vision
Predict Career Longevity for NBA Rookies using Scikit-learn
ChatGPT Prompt Engineering for Developers is a short course teaching how to use large language models (LLMs) to build new applications, engineer effective prompts,...