Gain insights into predicting retail sales with TensorFlow. Delve into data analysis, training models, and extracting insights using industry techniques. Explore efficient model evaluation with experts from top tech companies.

APLCS.tar.gz

case-study

In this course, you'll work on an industry-level machine learning project based on predicting weekly retail sales given different factors. You will learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this course, you will 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 course 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. Basic knowledge of Python and TensorFlow are prerequisites. To get some experience with TensorFlow, try our course: Machine Learning for Software Engineers.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Industry Case Study with TensorFlow

# Chapter Goals:
- Train the regression model

# A. Training with the `Estimator`

Since we set up all the code for the regression model, we can now train the model using the *train.tfrecords* file we created in the Data Processing Lab. The `Estimator` object contains a [`train`](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator#train) function that lets us easily train the model.

Interestingly, the `train` function's only required argument is a function that takes in no input arguments. This function should set up the input pipeline for the model training.

In our case, it will return the training dataset using the `create_tensorflow_dataset` function from the Data Processing Lab

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Model Training

Chapter Goals:

A. Training with the `Estimator`

Model Training

Chapter Goals:

A. Training with the Estimator

A. Training with the `Estimator`