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:
- Learn what goes inside the project's final report, and how it should be presented


## A. Creating the final report
After making the predictions on the testing dataset, we can create the final report for the project. The report contains all major parts of the project, including data analysis, model overview, and results from training, evaluation, and predictions. The best way to present the report is through a slideshow presentation, which allows us to convey results with meaningful visualizations (e.g. plots, charts, etc.). Each slide should have limited text.

The project report should focus more on the data and results, rather than the machine learning model. It's good to have an overview of the model's architecture, but avoid going too in-depth into how the model actually works. A short description of the model and diagram of the architecture should be enough.

To see what this project's report looks like, check out the presentation in the right-hand column of the screen.

# Chapter Goals:
- Learn what goes inside the project's final report, and how it should be presented


# A. Creating the final report
After making the predictions on the testing dataset, we can create the final report for the project. The report contains all major parts of the project, including data analysis, model overview, and results from training, evaluation, and predictions. The best way to present the report is through a slideshow presentation, which allows us to convey results with meaningful visualizations (e.g. plots, charts, etc.). Each slide should have limited text.

The project report should focus more on the data and results, rather than the machine learning model. It's good to have an overview of the model's architecture, but avoid going too in-depth into how the model actually works. A short description of the model and diagram of the architecture should be enough.

To see what this project's report looks like, check out the presentation in the right-hand column of the screen.

Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Final Report

Chapter Goals:

A. Creating the final report