{"id":4269644,"date":"2021-06-28T17:58:00","date_gmt":"2021-06-28T15:58:00","guid":{"rendered":"https:\/\/www.deptagency.com\/insight\/machine-learning-for-product-managers-101\/"},"modified":"2025-12-08T13:42:24","modified_gmt":"2025-12-08T11:42:24","slug":"machine-learning-for-product-managers-101","status":"publish","type":"article","link":"https:\/\/www.deptagency.com\/en-au\/insight\/machine-learning-for-product-managers-101\/","title":{"rendered":"Machine Learning for Product Managers – Machine Learning 101"},"content":{"rendered":"
\n\t\n\t<\/svg>\n\t\t\t\n\t\t\tBack to all articles\t\t<\/span>\n\t\n<\/button>\n\t\tMachine Learning for Product Managers – Machine Learning 101\t<\/h1>\n\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t<\/picture>\n\t\n\t<\/div>
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\n\t\tJesse Streb\t\t
\n\t\tGlobal SVP, Engineering & Technology\n\t\t\t<\/div>\n<\/div>\n\t
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\n\t\t\t\t\t28 June 2021\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n<\/div>\n\n
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\n\t\t\tMachine learning (ML) makes it possible for product managers to scale data analytics, expedite research, automate tasks, and alert users.\t<\/h2><\/div><\/div>\n\n

With a well-managed algorithm, you can easily enhance your product and grow your user base.

But how do you get started? Machine learning seems so mysterious. And that\u2019s because\u00a0it sort of is<\/em>\u00a0mysterious.

Even the algorithm creators don\u2019t fully understand \u201cwhy\u201d an algorithm selects something over another. For a product manager, it\u2019s a lot to comprehend. However, there are some things that are relatively simple to understand, and that\u2019s what we’re going to take you through in this 101 crash course for product managers. With this knowledge, you can communicate with your team effectively and work with them to find solutions.

Here are the most important things that product managers should know about machine learning.<\/p><\/div><\/div>\n\t<\/div>\n<\/div>\n\n

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\n\t\t\tMachine learning technology is really good at finding anomalies and patterns in data.\t<\/h2>\t<\/div>\n\n<\/div>\n\n
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\n\t\t\n\t\tMachine Learning 101 – What Is It?\t<\/h2>\n\t\t\t\t\t
\n\t\t\t\t\n\n\t\tMachine learning is sort of like The Oracle in the Matrix. <\/a>

You bring it a question and it gives you an accurate, yet secret, answer. <\/em>

A simple example might be determining whether pictures of people are smiling, neutral or frowning. A program would present an image to the machine learning algorithm and it would spit out an answer: smiling.

While people often associate ML with AI (artificial intelligence), they are not interchangeable terms. ML makes up a single part of the complex AI spectrum.

AI experts aim to create software and robots that mimic human behavior on a broad scale (for improved feedback\/services etc.), and ML is one subset\/application to realize this goal. There are various ML methods, depending on the type of processed data. However, all ML methods work by direct computation and applying data without a preset equation – similar to the human mind.
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