With interpretability becoming an increasingly important requirement for machine learning projects, there's a growing need to communicate the complex outputs of model interpretation techniques to non-technical stakeholders. SHAP (SHapley Additive exPlanations) is arguably the most powerful method for explaining how machine learning models make predictions, but the results from SHAP analyses can be non-intuitive to those unfamiliar with the approach. For those who wish to dig deeper on certain topics, links to useful resources are provided. Code for reproducing this analysis can be found on GitHub. SHAP is a method that explains how individual predictions are made by a machine learning model.

I have a challenge for you. In a few seconds, I want you to stop reading this article, and follow the instructions below. I hope my point is obvious. Machine learning and artificial intelligence (ML and AI) have seized Tech mindshare in a way few topics have in recent memory. A couple months ago I noticed people talking about artificial intelligence everywhere I looked.

According to McKinsey, AI will create an estimated $13 trillion of GDP growth between now and 2030. As a comparison, the GDP of the entire United States of America was around 19 trillion in 2017. Leading AI scientists, like Andrew Ng, describe AI as the fourth industrial revolution or „the new electricity". AI is undoubtedly a centerpiece of digital transformation and its application throughout the industry will dramatically change our world and how we do business. The problem is that many people want to participate in this AI-revolution but they are overwhelmed by its technological sophistication. They don't know what AI is capable of, let alone how they could use it for their company.

Machine learning is "[…] the branch of AI that explores ways to get computers to improve their performance based on experience". Let's break that down to set some foundations on which to build our machine learning knowledge. Branch of Artificial Intelligence: Artificial intelligence is the study and development by which a computer and its systems are given the ability to successfully accomplish tasks that would typically require a human's intelligent behavior. Machine learning is a part of that process. It's the technology and process by which we train the computer to accomplish the said task.

In last week's post, we discussed if machine learning was right for your business. As part of that effort, I recently went through the process of learning the ins-and-outs of machine learning and realized most information out there is technical and aimed at developers or data scientists. I thought an explanation from a non-technical person might be of interest. Machine learning is "[…] the branch of AI that explores ways to get computers to improve their performance based on experience". Let's break that down to set some foundations on which to build our machine learning knowledge.

I have a challenge for you. In a few seconds, I want you to stop reading this article, and follow the instructions below. I hope my point is obvious. Machine learning and artificial intelligence (ML and AI) have seized Tech mindshare in a way few topics have in recent memory. A couple months ago I noticed people talking about artificial intelligence everywhere I looked.

I have a challenge for you. In a few seconds, I want you to stop reading this article, and follow the instructions below. I hope my point is obvious. Machine learning and artificial intelligence (ML and AI) have seized Tech mindshare in a way few topics have in recent memory. A couple months ago I noticed people talking about artificial intelligence everywhere I looked.

In a few seconds, I want you to stop reading this article, and follow the instructions below. Machine learning and artificial intelligence (ML and AI) have seized Tech mindshare in a way few topics have in recent memory. A couple months ago I noticed people talking about artificial intelligence everywhere I looked. According to AI experts, everything from our jobs, to the wars we wage, to the food we eat, to the beer we drink, to the software we write will be affected. Not being one to enjoy surprises, I decided to spend my free time learning as much about the space (and what the future holds) as possible.

In last week's post, we discussed if machine learning was right for your business. As part of that effort, I recently went through the process of learning the ins-and-outs of machine learning and realized most information out there is technical and aimed at developers or data scientists. I thought an explanation from a non-technical person might be of interest. Machine learning is "[…] the branch of AI that explores ways to get computers to improve their performance based on experience". Let's break that down to set some foundations on which to build our machine learning knowledge.

In a few seconds, I want you to stop reading this article, and follow the instructions below. Machine learning and artificial intelligence (ML and AI) have seized Tech mindshare in a way few topics have in recent memory. A couple months ago I noticed people talking about artificial intelligence everywhere I looked. According to AI experts, everything from our jobs, to the wars we wage, to the food we eat, to the beer we drink, to the software we write will be affected. Not being one to enjoy surprises, I decided to spend my free time learning as much about the space (and what the future holds) as possible.