Domino Data Science Blog

Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn. ML models can be trained to benefit businesses in numerous ways, by quickly processing huge volumes of data, identifying patterns, finding anomalies or testing correlations that would be difficult for a human to do unaided.

Of all the thousands of algorithms available for machine learning, or ML, the vast majority use one of three main branches of learning techniques.

Computer vision is one of the most advanced and fascinating fields of data science, allowing computer technology to see as humans do and to represent data back to humans through visual imagery. While advances in computer vision have exploded just in the past couple of years, its roots and its first breakthroughs go back nearly 60 years.

While humans have been using language since we arose, a complete understanding of language is a lifelong pursuit that often comes short, even for experts. To task computer technology with comprehending language, translating and even producing original written works represents a series of problems that are still in the process of being solved.

When you do something well, you’re rewarded. This simple principle has guided humans since the beginning of time, and now, more than ever before, it is the key principle behind a growing number of reinforcement learning (RL) agents within the technologies we use and rely upon every day.

Phrases like deep learning and neural networks may be synonymous with artificial intelligence in the public’s minds, but for the data science teams that work with them, they are a breed of their own. Behind the dashboards of self-driving cars and below the online apps we use every day are a series of complex, interweaving algorithms that were inspired by the neurobiology of the human brain.