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:
- Evaluate the regression model

# A. Evaluating with the `Estimator`
We train the model long enough that the loss begins to show signs of convergence (for our 2 hidden layer MLP model, this is around 2M training steps). To get a better look at how the loss is progressing, we can take a look at the TensorBoard training visualizations (see the Model Execution section of [Deep Learning for Industry](/collection/6083138522447872/6598392304107520) for more details on TensorBoard).



What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Model Evaluation

Chapter Goals:

A. Evaluating with the `Estimator`

Model Evaluation

Chapter Goals:

A. Evaluating with the Estimator

A. Evaluating with the `Estimator`