4 Machine Learning algorithms and their real life use cases

Ensemble learning classification algorithms: RandomForests/Decision Trees

Ensemble learning algorithms are effectively being used for decades in a lot of areas for a different set of tasks: Analytics, Recommendation engine construction, Marketing Personalization, Fraud Detection.

Recommendation
You’re probably familiar with these concepts if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.

Marketing Personalization
The more you can understand about your customers, the better you can serve them, and the more you will sell. That’s the foundation behind marketing personalization. You would notice that sometimes you visit an online store and look at a product but don’t buy it and then see digital ads across the web for that exact product days afterward. That kind of marketing personalization is just the tip of the iceberg. Companies can personalize which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale.

Fraud Detection
Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

Regression models: Linear regression, logistic regression, RandomForests, Reinforcement Learning

The difference between regression and classification is that in classification task we predict predefined label, while in regression our goal is a number value. This may be predicting Sales, Housing Prices or Stock Prices

Financial Trading
Many people are eager to be able to predict what the stock markets will do on any given day — for obvious reasons. But machine learning algorithms are getting closer all the time. Many prestigious trading firms use proprietary systems to predict and execute trades at high speeds and high volume. Many of these rely on probabilities, but even a trade with a relatively low probability, at a high enough volume or speed, can turn huge profits for the firms. And humans can’t possibly compete with machines when it comes to consuming vast quantities of data or the speed with which they can execute a trade.

Product pricing
With thousands of products and many factors that impact sales, an estimate of the price to sales ratio or price elasticity is difficult. Dynamic price optimization using machine learning can help in this regard — correlating pricing trends with sales trends by using an algorithm, then aligning with other factors such as category management and inventory levels.

Recurrent Neural Networks: LSTM, GRU

One of the most well-known uses of Neural Networks applications is Sound based applications. With the proper data feed, Recurrent neural networks are capable of understanding audio signals. These can be:
Voice recognition — mostly used in IoT, Automotive, Security and UX/UI
Voice search — mostly used in Telecoms, Handset Manufacturers
Sentiment Analysis — mostly used in CRM
Flaw Detection (engine noise) — mostly used in Automotive and Aviation

Regarding common use cases, we are all familiar with voice-search and voice-activated assistants with the new wide spreading smartphones such as Apple’s Siri, Google Now for Android and Microsoft Cortana for Windows Phone.
Language understanding is another common use case for Voice Recognition. Speech-to-text applications can be used to determine snippets of sound in greater audio files, and transcribe the spoken word into text.

Convolutional Neural Networks

Convolutional Neural Networks or CNN achieve state of the art accuracy in Image Recognition, Video Segmentation, Video Object tracking tasks. There are tons of things can be built on top of these algorithms, Smart cars technology for example.

Smart Cars
IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet of Things, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.