Gain insights into predicting retail sales with TensorFlow. Delve into data analysis, training models, and extracting insights using industry techniques. Explore efficient model evaluation with experts from top tech companies.

APLCS.tar.gz

case-study

In this course, you'll work on an industry-level machine learning project based on predicting weekly retail sales given different factors. You will learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this course, you will be able to take on industry-level machine learning projects, from data analysis to creating efficient models and providing results and insights.

The code for this course is built around the TensorFlow framework, which is one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Basic knowledge of Python and TensorFlow are prerequisites. To get some experience with TensorFlow, try our course: Machine Learning for Software Engineers.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Industry Case Study with TensorFlow

# Chapter Goals:
- Process the embedding feature columns used for the machine learning model's input layer

# A. Feature embeddings

The other feature column for categorical features is embedding feature columns. For our dataset, the embedding features are `'Store'` and `'Dept'`. Both these features have many more categories than the indicator features, so using a one-hot vector representation wouldn't necessarily be the most efficient or useful.

Furthermore, there are connections between categories for both the `'Store'` and `'Dept'` features. For example, we may see similar sales trends between stores in the same region or between departments that sell complementary items (e.g. milk and cereal).

These are trends that can be encapsulated by an embedding feature column, which maps each category to a real-number vector of a specified dimension. We can set the vector dimension to anything, but a good rule of thumb is to set it equal to the $4^{th}$ root of the number of categories.

Learn about the embedding feature columns for the ML model's input layer.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Embedding Columns

Chapter Goals:

A. Feature embeddings