ML-For-Beginners/Real-World/1-Applications

Machine Learning in the Real World

In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, Natural Language Processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for...what are the real world applications for these models?

While a lot of interest in industry has been garnered by AI, which usually leverages Deep Learning, there are still valuable applications for classical machine learning models, some of which you use today, although you might not be aware of it. In this lesson, you'll explore how ten different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and thus more valuable to users.

Pre-lecture quiz

Finance

One of the major consumers of classical machine learning models is the finance industry.

Credit card fraud detection

Wealth management

Education

Predicting student behavior

Preventing plagiarism

Course recommendations

Retail

Personalizing the customer journey

Inventory management

Health Care

Optimizing drug delivery

Hospital re-entry management

Disease management

Ecology and Green Tech

Forest management

Motion sensing of animals

Energy Management

This article discusses in detail how clustering and time series forecasting help predict future energy use in Ireland, based off of smart metering: https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf

Insurance

Actuarial tasks

Consumer Electronics

Motion sensing

Software

UI regression

Document search

Recommendation engines

Arts, Culture, and Literature

Fake news detection

Classifying artifacts

Marketing

'Ad words'

Customer segmentation

✅ Knowledge Check - use this moment to stretch students' knowledge with open questions

🚀Challenge

Add a challenge for students to work on collaboratively in class to enhance the project

Post-lecture quiz

Review & Self Study

Assignment: Assignment Name