What is the difference between Deep Learning and Machine Learning for a business analyst?

All answers to this question.

Think of Machine Learning (ML) as the umbrella category, and Deep Learning (DL) as a specialized, more complex version of it. For a Business Analyst, ML is usually about structured data—like predicting sales using Excel-like tables. Deep Learning is for "unstructured" data like images, speech, or complex text. You don't need to be a mathematician, but you do need to know that DL requires much more data and computing power (GPUs) to be effective. For automating customer insights from text or reviews, DL-based Natural Language Processing (NLP) is significantly more powerful than traditional ML algorithms.

   Answered 2024-06-20 by Mary Higgins

Since Deep Learning requires so much more data, at what point does a small to medium-sized business decide to switch from standard ML to a Deep Learning model?

   Answered 2024-07-05 by Robert Brown

• Robert, usually you make the switch when your traditional models "plateau"—meaning adding more data no longer improves the accuracy. For a medium business, if you are just doing churn prediction based on customer age and location, stick with ML. If you want to build a chatbot that truly understands customer emotion or a visual search tool for your inventory, that is when you must invest in DL. It’s a cost-benefit analysis where the extra accuracy must outweigh the significantly higher infrastructure and talent costs required.

     Commented 2024-07-12 by Christopher Davis

For most BA roles, focusing on "Explainable AI" (XAI) is more important. You need to be able to explain to stakeholders why the model made a specific prediction, which is often harder in DL.

   Answered 2024-08-18 by Linda Garcia

• Linda is spot on. Stakeholders hate "black box" models. If you can bridge the gap between the complex neural network and a simple business reason, you become indispensable.

     Commented 2024-08-20 by Jennifer Lopez