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 section of the course you will be performing data processing on the final dataset from the **Preliminary Data Analysis** section. Specifically, you'll be building the input pipeline for training and evaluating the machine learning model.



# A. Additional data processing

In the **Preliminary Data Analysis** section, we performed data analysis on the retail dataset and concluded that there is a strong enough correlation from the dataset's features to predict weekly store sales. 

The files in that chapter were small enough to use relatively simple techniques. If the files were larger and more resource intensive, we would have used the techniques laid out in the **Efficient Data Processing Techniques** section.

After informing the project supervisor that the prediction task is viable, we begin working on the machine learning model.

However, before writing any actual machine learning code, we know that we need to continue processing the data to create an efficient *input pipeline*. The input pipeline represents how the data will be passed into the model for each step of training or evaluation. Since training the model requires thousands of steps, it is important that the input pipeline is as efficient as possible.

The final dataset we created was stored in a pandas DataFrame. Since the DataFrame is not the most efficient data storage for the input pipeline, we'll need to perform additional processing to create a more efficient solution.


What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Introduction