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

Congratulations, you have completed the third module!
## Summary
We started with preliminary data analysis and processing. We then created and analyzed the predictions of a machine learning model for a real-world project. This module has provided an overview of data analysis/visualization. We also learned about feature engineering, data pipeline and supervised learning.

## What's next?
Now that we have mastered the theoretical and practical aspects of machine learning, Let’s prepare for the PyTorch.


Congratulations, you have completed the third module!
# Summary
We started with preliminary data analysis and processing. We then created and analyzed the predictions of a machine learning model for a real-world project. This module has provided an overview of data analysis/visualization. We also learned about feature engineering, data pipeline and supervised learning.

# What's next?
Now that we have mastered the theoretical and practical aspects of machine learning, Let’s prepare for the PyTorch.


Here's a quick summary of the third module.

Preliminary Data Analysis

Data Processing

Model Predictions

Conclusion

Final Remarks

Summary