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:
- Set up the regression function's prediction code

# A. Prediction values
For the prediction mode in the regression function, we initialize and return an `EstimatorSpec` object containing a dictionary with the model's predictions. The model's predictions need to be in 2-D tensor format, with shape `(batch_size, 1)`.

Using the 1-D tensor version (which was used in calculating the loss) will result in an indexing error when making predictions on a TFRecords dataset.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Prediction Mode

Chapter Goals:

A. Prediction values