What Is Machine Learning?

Machine learning is a type of artificial intelligence that allows computer technology to perform like humans—learning from people’s experiences to improve their accuracy over time. An IBM employee—Arthur Samuel—coined the term in 1962 and applied the technology to “teach” a computer to play chess. The computer ultimately beat him, proving the ability of technology to act like humans would. 

What’s the difference between ML, NLP, and AI? 

The terms machine learning (ML), natural language processing (NLP), and artificial intelligence (AI) are often misunderstood and used interchangeably. While the terms are interconnected, they are distinct thanks to a couple of core differences:

• AI provides the foundation for both ML and NLP. Artificial intelligence is a computer technology that allows machines to operate in ways that would normally require human intelligence for problem-solving, decision-making, perception, and understanding language (written and spoken).

• Machine learning is a subset of AI. It allows machines to learn and make predictions or decisions based on data. Machine learning learns from the data it is exposed to, improving performance over time.

• NLP is another specialized subset of AI that allows computers to read, understand, and interpret human language—paving the way for the use of chatbots and virtual assistants (like Siri and Alexa).

What are the three types of machine learning? 

According to MIT Management, there are three subcategories of machine learning:

• The most common, supervised machine learning, trains machine learning models with labeled data sets, allowing the models to learn over time.

• Unsupervised learning is where machine learning models find patterns or trends that humans did not specifically instruct them to look for.

• Reinforcement learning describes the process of machine learning learning over time through trial and error, essentially being “trained” to perform better over time.

How is machine learning used in HR?

Machine learning has a wide range of applications in HR and other professions alike. For instance, machine learning can be used during talent acquisition to evaluate applications and resumes and select those most likely to succeed in the open position. It can also be used in performance development to identify topics and training programs certain employees could benefit from in advancing their careers. 

How does Visier use machine learning?

Visier uses a best-practice machine learning technique called random forest. It’s a technique that involves the analysis of historical employee data and events—like promotions, resignations, and internal hires—to identify patterns and construct decision trees that help clients predict future events. 

Read more on machine learning:    

• How To Use AI in HR

• Fact or Hype: Validating Predictive People Analytics and Machine Learning

• What Is Augmented Analytics?