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

We can check if the dataset has missing values, and if so we don't use the dataset

We can determine whether there are actual trends that can be captured by a model

It is important to do from a legal standpoint prior to using data

Why is data analysis important before creating a machine learning model for a task?

Why is data analysis important before creating a machine learning model for a task?

Don't use that particular feature in the dataset

Fill in the missing values based on the other data values in the feature

Both A and B are viable options, depending on the data

When dealing with incomplete feature data, how should we deal with the missing values?

When dealing with incomplete feature data, how should we deal with the missing values?

If the y-axis (dependent) feature in the plot is a numerical feature

If the x-axis (independent) feature in the plot is a categorical feature

If the x-axis (independent) feature has missing values

If both the x-axis and y-axis features are categorical features

What's the most likely scenario where we would use a bar plot rather than a scatter plot or line plot?

What’s the most likely scenario where we would use a bar plot rather than a scatter plot or line plot?

Feature values on the x-axis that are unused or have many points

A particular distribution of the data, e.g. normal and uniform distributions

Peaks and craters in the data that show potentially significant feature values

What are some trends we look for when analyzing data plots?

What are some trends we look for when analyzing data plots?

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Quiz