/ Understanding different machine learning techniques

Understanding different machine learning techniques

In order to best understand how machine learning (ML) can impact your business, it’s important to look at some of the different learning techniques you can utilize within ML. Some may be more appropriate for your business than others, some may be used depending on what type of analysis you’re trying to do, and some may be combined for the best effect on your data analysis and predictions.

First, the basics. ML in data science is a way to use computer algorithms and statistical models to analyze data and create accurate predictions. So all you need is some data and a tool, like Domo’s AutoML, and you can start using AI as part of your data science.

But there is a lot that goes on behind the scenes and understanding the different learning techniques in ML will help you gain a greater foundation on how to use ML within data science. The speed at which everything in AI is changing, including techniques for using ML, means that it’s important to have a good grasp of the fundamentals.

 

5 types of machine learning

There are many different ways to train algorithms and develop ML models. Here are 5 ways and how they can be used within data science:

Multi-task learning

This is where you’re developing ML models that are learning from multiple tasks at the same time. The model is improved because it can apply what it learns from one task to another, rapidly improving performance across all tasks. The results tend to be more accurate and learn faster across all the models than if each model had been trained separately.

 

 

This is helpful when you don’t have a lot of labeled data up front, and there are too many variables within the data to apply rules globally.

One common example is spam filters. Users tend to not provide enough examples of what they consider spam, so not enough data is labeled up front to easily identify spam filters. And some messages are spam for one user, while it may not be for another user: a message in Spanish would be spam for an English-speaking user, but not for a Spanish-speaking user. Therefore a spam filter needs multiple tasks running simultaneously that can learn from each other on how to identify a spam message.

Active learning

Active learning is an approach to developing models that are learning with human trainers. The model identifies unlabelled data and asks the human trainer to identify it. As data is labeled the model learns and grows more efficient.

The idea is that the model is more efficient and highly accurate. This can be helpful when you don’t have much data available in your dataset but require highly accurate results.

Online learning

Use online learning models to keep up with data that is changing rapidly. With online learning, you’re developing models that update as each new data point arrives.

 

 

This model is helpful in environments where your model is supposed to change with your data when the data is changing consistently over a long period of time. These can be datasets that grow slowly or real-time data environments where your data is growing rapidly.

The idea is that you are getting the most up-to-date information from your model and that it’s learning from all the available data. With real-time data, even if you updated your model manually every data you’d be lagging on having the most comprehensive and recent analysis. Online learning helps you get near real-time insights.

Speed is king when working with this learning technique, so it typically has simple algorithms driving it.

Transfer learning

Using the same concept behind the idea that once a human learns a task, it’s easier to learn a similar task (i.e. walking and cross-country skiing), transfer learning applies that to ML models. When you’ve trained a model to complete one task, you can reuse some or all of the model to quickly be trained on a similar task.

This is helpful when you don’t have a lot of data to start with training a model. It’s also helpful when it takes a long time to train a model for a specific task, you can increase the ROI of that effort by applying it across multiple tasks.

One common example is text analysis. You can train a model to recognize tone on some passages of text, say a classic novel. You can reapply most of that model to either begin to recognize other aspects of language, like intent, or apply it to a different type of text, like social media.

Ensemble learning

Much like how each piece of a band comes together to form a whole song, ensemble learning puts multiple ML models together and combines the results for one prediction. The idea is that you can have a more accurate prediction when using multiple sources.

 

 

Use this learning technique when the accuracy and quality of your predictions are paramount. It helps to reduce variance and bias within the models, averaging and proving predictions against each other.

 

Using different learning techniques in your business

How would you use these models in your business? Especially within data science? Many of these tools are already available on the market providing recommendations, spam filtering, or content displays, as part of a product or service. What would it look like to use them to provide analysis and predictions?

You could use transfer learning to train a model to recognize objects in images and classify that data for easy analysis. Or you could apply an online learning technique to quickly analyze website traffic and visits to highlight the most popular pages and provide real-time predictions on web flow. Consider using multi-task learning to efficiently label and categorize data from sales tools to create a more accurate forecast.

However you choose to use ML techniques, understanding the driving forces behind ML tools will help you grow the effectiveness of how you implement AI in data science.

Check out some related resources:

Machine learning basics: Predictive analytics vs. Machine learning

Natural language processing and data science workflows

The future of business intelligence and automated machine learning

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