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:
- Analyze the categorical features in the dataset

## A. The dataset format

When using the dataset to train a machine learning model, each feature needs to be an integer, float, or string type. The float data is the numeric data, i.e. the data that can be quantified and analyzed using operations like mean or standard deviation. The string data is categorical, meaning that each string represents some unique category for the feature. Integer data can be either numeric (e.g. kilometer distance) or categorical (e.g. year of birth).

In the final dataset we're using, the categorical features are the `'Store'`, `'Type'`, and `'Dept'` features. Since we already know there are 45 stores, labeled from 1 to 45, we just need to investigate the `'Type'` and `'Dept'` features.


# Chapter Goals:
- Analyze the categorical features in the dataset

# A. The dataset format

When using the dataset to train a machine learning model, each feature needs to be an integer, float, or string type. The float data is the numeric data, i.e. the data that can be quantified and analyzed using operations like mean or standard deviation. The string data is categorical, meaning that each string represents some unique category for the feature. Integer data can be either numeric (e.g. kilometer distance) or categorical (e.g. year of birth).

In the final dataset we're using, the categorical features are the `'Store'`, `'Type'`, and `'Dept'` features. Since we already know there are 45 stores, labeled from 1 to 45, we just need to investigate the `'Type'` and `'Dept'` features.


Learn about categorical data and how it is used in a dataset.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Categorical Data

Chapter Goals:

A. The dataset format