# 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](link-to-quiz-app)

## Finance

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

### Credit card fraud detection

We learned about [k-means clustering](Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?

https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf

### 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](link-to-quiz-app)

## Review & Self Study

**Assignment**: [Assignment Name](assignment.md)