Machine Learning for Supply Chains Specialization

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.

Instructors: Neelesh Tiruviluamala +1 more

Skills you'll gain

  •   NumPy
  •   Data Visualization
  •   Trend Analysis
  •   Time Series Analysis and Forecasting
  •   Data Wrangling
  •   Exploratory Data Analysis
  •   Feature Engineering
  •   Supervised Learning
  •   Applied Machine Learning
  •   Demand Planning
  •   Predictive Modeling
  •   Data Manipulation
  • Specialization - 4 course series

    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.

    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.

    Demand Forecasting Using Time Series

    Advanced AI Techniques for the Supply Chain

    Capstone Project: Predicting Safety Stock

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