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:
- Create the generalizable function for the MLP model's layers

# A. MLP architecture
Our project's model follows the standard MLP architecture. This means that it is made up of multiple fully-connected layers, where each hidden layer uses ReLU activation and the final layer uses no activation. The input layer for the MLP consists of a batch of data observations from the input pipeline (more on this later).


Larger models (i.e. more hidden layers and nodes) have higher potential to make more accurate predictions, but they can also take longer to train and have a higher chance of overfitting. It's good to experiment with different model sizes, so we can ultimately choose the best model. This is why we use an evaluation set in addition to the training set, to compare different model configurations and see which configuration performs best on new data.

For our MLP model, we'll start off with 2 hidden layers. The first hidden layer contains 200 nodes, while the second contains 100. This equates to the list `[200, 100]` for initializing the `SalesModel` class object.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Model Layers

Chapter Goals:

A. MLP architecture