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:
- Figure out exactly how many missing values are in each feature
- Drop the features that contain too many missing values

# A. Counting the missing values

In the previous chapter, we figured out that each of the `'MarkDown'` features, along with the `'CPI'` and `'Unemployment'` features contained missing values. We now want to figure out how many missing values each of these features has, i.e. how many rows of the combined feature DataFrame don't contain a value for the particular feature.

This can be done by counting the number of `True` values for each feature's column in the boolean DataFrame.


Drop features from the dataset that have too many missing data values.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Dropping Features

Chapter Goals:

A. Counting the missing values