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:
- Write the training and evaluation set data into TFRecords files

# A. Writing Example data

Now that we've completed the function to convert each DataFrame row into an Example object, we can create the efficient input pipeline storage for both the training and evaluation sets. The data storage will be in the form of TFRecords files, which hold serialized Example objects.

The `write_tfrecords` function (shown below) writes the data from a given DataFrame into a TFRecords file. It uses the `create_example` function from the previous chapter to convert each row of the dataset into an Example object. Each Example object is then serialized and written into the TFRecords file.

Write serialized Example objects into TFRecords files.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Writing TFRecords

Chapter Goals:

A. Writing Example data