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

In this Lab you will be building the machine learning model for predicting sales. Then, after training and evaluation to create the final model, you'll use it to make sales predictions on new data.


# A. Which model do we use?
Now that we've analyzed and processed the project's data, we need to choose the correct model for the task. While certain applications benefit from more advanced models (e.g. [CNNs for image recognition](/collection/6083138522447872/5093907643760640), [LSTMs for NLP](/collection/6083138522447872/5255772847996928/)), a majority of use cases can be handled by a simple [multilayer perceptron](/courses/machine-learning-for-software-engineers).

Our case study deals with predicting weekly sales for various retail stores. The feature data used in the model's input pipeline consists of regular numeric and categorical features, meaning that an MLP will work just fine in making predictions.


What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Introduction

A. Which model do we use?