Here is a Non-Technical Introduction to Machine Learning


Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it.

I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field.

This is for smart, ambitious people who want to know more about machine learning but who don’t care about the esoteric statistical and computational details underlying the field. You don’t need to know any math, statistics, or computer science to read and understand it.

By the end of this post, you’ll:

  1. Understand the basic logical framework of machine learning (ML).
  2. Be able to define important relevant terms and concepts that anyone interested in this field should know. These terms are highlighted in boldface.
  3. Know which high-level decisions go into building statistical models, and understand some of the implications of these decisions.
  4. Be able to better analyze the question of when we should use the results of ML to make big decisions, such as determining public policy.

This overview is in no way comprehensive. Huge portions of the field are left out, either because they are too rare to merit study by non-technical decision makers, because they’re difficult to explain, or both.

What is machine learning?

The field itself: ML is a field of study which harnesses principles of computer science and statistics to create statistical models. These models are generally used to do two things:

  1. Prediction: make predictions about the future based on data about the past
  2. Inference: discover patterns in data

Difference between ML and AI: There is no universally agreed upon distinction between ML and artificial intelligence (AI). AI usually concentrates on programming computers to make decisions (based on ML models and sets of logical rules), whereas ML focuses more on making predictions about the future.

They are highly interconnected fields, and, for most non-technical purposes, they are the same.

What’s a statistical model?

Models: Teaching a computer to make predictions involves feeding data into machine learning models, which are representations of how the world supposedly works. If I tell a statistical model that the world works a certain way (say, for example, that taller people make more money than shorter people), then this model can then tell me who it thinks will make more money, between Cathy, who is 5’2”, and Jill, who is 5’9”.

Read the source article at the SafeGraph blog.