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:
- Create the `Estimator` object for the regression model

# A.`Estimator` object
The entire regression model, from training to evaluation to predictions, can be encapsulated in a single [`Estimator`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator) object. The `Estimator` object is initialized with the regression function, as well as a few keyword arguments.

One of the keyword arguments is `model_dir`, which represents the path to the directory that will contain the model's checkpoints. The checkpoints are how we save and restore the model's parameters for training, evaluation, and making predictions.

Another keyword argument we'll use is `config`, which specifies a custom configuration for the model. We can customize values like logging frequency, the maximum number of checkpoints to keep, and how often we save a summary of the training. These can all be set by creating a [`RunConfig`](https://www.tensorflow.org/api_docs/python/tf/estimator/RunConfig) object with the designated values.

For our regression model, the only custom configuration we'll set is the logging frequency. This refers to how frequently the model will log the loss and global step values to the screen during training. The default frequency is every 100 training steps, but since our model's training steps are very quick, we'll set the frequency to every 5000 steps.


Regression Model

Chapter Goals:

A.Estimator object

A.`Estimator` object