Monthly Subscription Starting at AED 99 + VAT

Advanced AI Techniques for the Supply Chain
  • Level Professional
  • Duration 22 hours
  • Course by LearnQuest
  • Offered by

About

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.

Modules

From Basic to Advanced: Models you Need to Know

    • 3 Videos
    • 2 Readings
Show info about module content

3 Videos

  • Course Intro
  • Module Intro
  • Overview of AI methods

2 Readings

  • Supervised and Unsupervised Learning Techniques
  • Forbes: ML Revolutionizing the Supply Chain

Introduction to Neural Networks

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Neural Networks

1 Discussions

  • Machine Learning Use Cases

1 Videos

  • Introduction to Neural Networks

1 Readings

  • Neural Networks Playground

Deep Dive into Neural Networks

    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Implementing Neural Networks

1 Readings

  • Optional: Math Behind Neural Networks

Quiz: Neural Network Basics For the Supply Chain

    • 1 Assignment
Show info about module content

1 Assignment

  • Neural Network Basics For the Supply Chain

Picking a Model

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Module Intro
  • Choosing an AI Model

1 Readings

  • Model Selection

Loss Functions and Optimization

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Gradient Descent

1 Videos

  • Loss Functions

1 Readings

  • Stochastic Gradient Descent

Overfitting/Underfitting

    • 2 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Overfitting/Underfitting

2 Readings

  • Math Behind Bias-Variance Tradeoff
  • Configuring the Learning Rate

Programming Assignment: Coding Advanced AI Models

    • 1 Lab
    • 1 Programming
    • 1 Assignment
Show info about module content

1 Programming

  • Coding Advanced AI Models

1 Assignment

  • Coding Advanced AI Models

1 Labs

  • P​rogramming Assignment Solutions

Natural Language Preprocessing

    • 2 Videos
    • 2 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Autoencoders

2 Videos

  • Module Intro
  • Autoencoders

2 Readings

  • Accenture: Natural Language Processing Techniques
  • Autoencoders (Optional)

Analyzing Images

    • 2 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
Show info about module content

1 Discussions

  • Automating the ML Pipeline

1 Labs

  • Analyzing Images

2 Videos

  • Notebook Example of Loading Images
  • Notebook Example of CNNs

1 Readings

  • Image Data Analysis Using Python

Making Predictions on Images

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Convolutional Neural Networks

2 Readings

  • Convolutional Neural Networks (CNNs)
  • Convolution and Pooling Layers

Quiz: Images and Text

    • 1 Assignment
Show info about module content

1 Assignment

  • Images and Text

PA: Predicting on Images and Text

    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Predicting Digits Using the MNIST Dataset

1 Labs

  • P​rogramming Assignment Solutions
Rajvir Dua
Instructors

Rajvir Dua

Neelesh Tiruviluamala
Instructors

Neelesh Tiruviluamala