Embedding

This course explores the fascinating concept of Embeddings within Unsupervised Learning, a powerful tool for transforming complex data into a more manageable and meaningful format for Supply Chain Management (SCM) applications.

• Explain the concept of Embeddings and their role in Unsupervised Learning.

• Understand different Embedding techniques like Word2Vec and GloVe.

• Explore applications of Embeddings in analyzing SCM data (e.g., product recommendations, text analysis of customer reviews).

• Implement basic Embedding techniques using Python libraries (hands-on coding exercises).

• Evaluate the effectiveness of Embeddings for representing and analyzing SCM data.

1. Embeddings in Unsupervised Learning

• Introduction to Embeddings: Capturing Relationships in High-Dimensional Data.

• Deep dive into Word2Vec and GloVe: Understanding their functionalities and underlying concepts.

• Visualizing Embeddings: Projecting high-dimensional data into lower dimensions.

• Hands-on Coding Exercises: Implementing Word2Vec or GloVe on a sample SCM dataset (e.g., product descriptions).

• Case Studies: How Embeddings enhance analysis of customer reviews, product recommendations, and text-based data in SCM.

• Course Wrap-up: Discussion on the potential and limitations of Embeddings in SCM applications.

• Solid understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python, including experience with deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with unsupervised learning concepts and dimensionality reduction techniques