Combine what you've learned so far to analyze a real-world case from start to finish.

Machine Learning with Python Libraries

In this module, we'll work on an industry-level machine learning project based on predicting weekly retail sales with different given factors.  We’ll learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this module, we’ll 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 module 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. This module requires basic knowledge of Python and TensorFlow as its prerequisites. By the end of this module, we’ll have hands-on experience in creating machine learning projects.

TensorFlow

## Chapter Goals:
- Aggregate the feature columns for the machine learning model's input layer

## A. The input layer

The reason we create feature columns for each of the input data features is so that we can easily make the input layer vector for the machine learning model. The input layer is simply the combination of the numeric, indicator, and embedding feature columns.


# Chapter Goals:
- Aggregate the feature columns for the machine learning model's input layer

# A. The input layer

The reason we create feature columns for each of the input data features is so that we can easily make the input layer vector for the machine learning model. The input layer is simply the combination of the numeric, indicator, and embedding feature columns.


Understand the purpose of using feature columns for the model's input layer.

Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Model Input Layer

Chapter Goals:

A. The input layer