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:
- Add the integer features of a DataFrame's row to a feature dictionary

# A. Using Example objects

Each row of the final pandas DataFrame from the Data Analysis Lab contains the feature data for one data observation, i.e. the feature data for one store's sales in a particular week. To optimize the input pipeline, we want to convert each DataFrame row into a TensorFlow Example object. By using Example objects in the input pipeline, we're able to efficiently feed the data into a machine learning model.

After converting a DataFrame row to a TensorFlow Example, the row's integer valued features will be represented by `Int64List` TensorFlow Feature objects. From the analysis of our dataset, we know that the features with integer values are `'Store'`, `'Dept'`, `'IsHoliday'`, and `'Size'`.


Learn about the integer features used in the dataset.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Integer Features

Chapter Goals:

A. Using Example objects