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. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
The finance sector offers many opportunities for machine learning. Many problems in this area lend themselves to be modeled and solved by using ML.
金融领域为机器学习提供了许多机会。该领域的许多问题都可以通过使用机器学习来建模解决。
### Credit card fraud detection
### 信用卡欺诈检测
We learned about [k-means clustering](../../5-Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?
K-means clustering comes in handy during a credit card fraud detection technique called **outlier detection**. Outliers, or deviations in observations about a set of data, can tell us if a credit card is being used in a normal capacity or if something unusual is going on. As shown in the paper linked below, you can sort credit card data using a k-means clustering algorithm and assign each transaction to a cluster based on how much of an outlier it appears to be. Then, you can evaluate the riskiest clusters for fraudulent versus legitimate transactions.
In wealth management, an individual or firm handles investments on behalf of their clients. Their job is to sustain and grow wealth in the long-term, so it is essential to choose investments that perform well.
One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](../../2-Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out the paper below on evaluating fund performance using regression.
The educational sector is also a very interesting area where ML can be applied. There are interesting problems to be tackled such as detecting cheating on tests or essays or managing bias in the correction process, unintentional or not.
[Coursera](https://coursera.com), an online open course provider, has a great tech blog where they discuss many engineering decisions. In this case study, they plotted a regression line to try to explore any correlation between a low NPS (Net Promoter Score) rating and course retention or drop-off.
[Grammarly](https://grammarly.com), a writing assistant that checks for spelling and grammar errors, uses sophisticated [natural language processing systems](../../6-NLP/README.md) throughout its products. They published an interesting case study in their tech blog about how they dealt with gender bias in machine learning, which you learned about in our [introductory fairness lesson](../../1-Introduction/3-fairness/README.md).
The retail sector can definitely benefit from the use of ML, with everything from creating a better customer journey to stocking inventory in an optimal way.
无论是创造更好的客户旅程，还是以最佳方式管理库存，零售业绝对可以从机器学习的使用中受益匪浅。
### Personalizing the customer journey
### Personalizing the customer journey
@ -62,7 +62,7 @@ Innovative, nimble companies like [StitchFix](https://stitchfix.com), a box serv
The health care sector can leverage ML to optimize research tasks and also logistic problems like readmitting patients or stopping diseases from spreading.
The health care sector can leverage ML to optimize research tasks and also logistic problems like readmitting patients or stopping diseases from spreading.
@ -84,7 +84,7 @@ The recent pandemic has shone a bright light on the ways that machine learning c
Nature and ecology consists of many sensitive systems where the interplay between animals and nature come into focus. It's important to be able to measure these systems accurately and act appropriately if something happens, like a forest fire or a drop in the animal population.
Nature and ecology consists of many sensitive systems where the interplay between animals and nature come into focus. It's important to be able to measure these systems accurately and act appropriately if something happens, like a forest fire or a drop in the animal population.
@ -110,7 +110,7 @@ In our lessons on [time series forecasting](../../7-TimeSeries/README.md), we in
The Wayfair data science team has several interesting videos on how they use ML at their company. It's worth [taking a look](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)!
The Wayfair data science team has several interesting videos on how they use ML at their company. It's worth [taking a look](https://www.youtube.com/channel/UCe2PjkQXqOuwkW1gw6Ameuw/videos)!
ganyuanhao
4 years ago