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

In this case study, you will be completing a real industry machine learning project. The project involves making sales predictions (sales forecasting) for various stores of a large retail corporation, based on a variety of different features. Along the way, you will also learn techniques for efficient data processing and model training.



## A. Industry ML projects

Machine learning projects in industry are comprised of five main parts: data analysis, data processing, creating a model, training the model, and interpreting results. Data analysis is used to confirm that machine learning can actually find useful trends in the dataset to make the requisite predictions. Oftentimes, people will forget to perform data analysis and end up confused why their machine learning model doesn't work well, when in reality it is because the dataset is not conducive to making predictions.


# A. Industry ML projects

Machine learning projects in industry are comprised of five main parts: data analysis, data processing, creating a model, training the model, and interpreting results. Data analysis is used to confirm that machine learning can actually find useful trends in the dataset to make the requisite predictions. Oftentimes, people will forget to perform data analysis and end up confused why their machine learning model doesn't work well, when in reality it is because the dataset is not conducive to making predictions.


Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Introduction

A. Industry ML projects