Machine Learning, or ML, is a technology that uses software and algorithms to make these predictions. Luckily, to get your business using ML, you don’t need to know the nitty-gritty details of the stuff.

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Instead, leave the software engineering to the software engineers while you focus on how to start using machine learning in your products and strategy to make a more streamlined experience for customers.

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There are 3 basic levels for using ML in your business:
1. Improve an existing feature
2. Enable new features
3. Enable entirely new products

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Think big, like driverless cars

Since machine learning is still a relatively new thing, the way that most businesses will incorporate it will fall into level 1


To improve an existing feature through the application of machine learning, you should work

Even though it’s just the first level, the skills you’ll use to implement machine learning at this stage are the same ones you’ll build off if you end up using ML in more advanced ways.

While you don’t need to know exactly how ML algorithms work, it’s good to know what general types of systems ML can create to make your work easier.

First, there are recommender systems, which take a full collection of items (known as a “corpus”) and rank them. For instance, a streaming service might recommend movies from its collection based on a user’s viewing history.

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Event or action prediction models try to predict the likelihood of an event or user action. For example, an online shop might predict if and when a user will close the page without checking out, and offer a coupon as an incentive to stay.

Classification models

classify random objects into set groups. For instance, an email platform may categorize emails as “spam” or “not spam,” or a photo app could identify if an image contains a dog or not.

Generative models

can take the input you provide and generate the right output. For instance, a generative model trained to translate can look at the context of a text in one language and produce a correctly worded output in another.

clustering models

can segment users into groups. For example, a flower company might cluster users into orchid lovers and rose lovers and target those groups with different promotional emails.

Of course, once you make improvements to a feature, you’ll want to make sure that it still works and does what it’s supposed to do.

Enable New Features

An organization can enable new features in their product like most android phones have enabled face recognition app in their product. With the event of ML automation of product & service has been easier than before. You may find out that most time before you finish typing a word you have the full sentence of what you intend to types