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Applied Machine Learning: Industry Case Study with TensorFlow

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.

4.5

38 Lessons

Updated this week

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LEARNING OBJECTIVES

Load and inspect multi-CSV retail sales datasets using pandas to understand schema and key features
Clean datasets by detecting missing values, dropping high-missing features, and imputing CPI/Unemployment
Merge sales, features, and store data on shared keys (Store, Date, IsHoliday) to form a final dataset
Visualize and interpret feature relationships with Weekly_Sales using scatter and bar plots
Build scalable TFRecords input pipelines by serializing, writing, and parsing TensorFlow Examples
Create TensorFlow feature columns (numeric, indicator, embedding) and assemble a model input layer
Train, evaluate, and predict Weekly_Sales with a TensorFlow Estimator regression MLP and report results

Learning Roadmap

38 Lessons2 Quizzes16 Challenges

What you'll learn from this course

Get familiar with applying machine learning to real-world projects, from data processing to presentation.

Overview

Preliminary Data Analysis

Look at the foundational aspects of data analysis, handling missing data, visualizing trends, and interpreting plots.

Data Processing

14 Lessons

Work your way through building an efficient data input pipeline using TensorFlow for machine learning.

Model Predictions

12 Lessons

Break down the steps to developing, training, evaluating, and predicting with a regression model.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Show License and Attributions

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Adaptilab

AdaptiLab is a Seattle-based SaaS startup helping companies grow their machine learning teams from hiring to productivity.

Learn more about Adaptilab

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