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:
- Create a bar plot to visualize the correlation between dataset features and weekly sales

# A. Categorical feature plots

For categorical features, it doesn't make sense to create a scatter plot. Instead, when we're using a categorical feature as the independent variable (with a numeric feature as the dependent variable), we'll make a bar plot.

When using weekly sales as the dependent variable, we'll create bar plots that show the average weekly sale amount for each category in a categorical feature.


Learn how to create bar plots comparing categorical features to sales.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Bar Plot

Chapter Goals: