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:
- Process the indicator feature columns used for the machine learning model's input layer

# A. One-hot indicators

The remaining non-numeric features in the dataset are the categorical features. Each of these features contains values that can be separated into a fixed number of distinct categories. For example, the `'IsHoliday'` feature contains the categories `0` and `1`, while the `'Type'` feature contains the categories `A`, `B`, and `C`.

These two features specifically (`'IsHoliday'` and `'Type'`) will be the indicator features for the dataset. Since indicator features are categorical, this means they will be represented by [`one-hot`](https://en.wikipedia.org/wiki/One-hot) vectors when aggregated into the model's input layer.



Learn about the indicator feature columns for the ML model's input layer.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Indicator Columns

Chapter Goals:

A. One-hot indicators