Artificial Intelligence for Business

Artificial Intelligence and its abilities


  • No experience needed. You will learn everything you need to know.


AI touches every aspect of our personal and professional online lives today. Global communication and interconnectivity in business is, and continues to be, a hugely important area. Capitalising on artificial intelligence and data science is essential, and its potential growth trajectory is limitless

oday, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making.

On a far grander scale, AI is poised to have a major effect on sustainability, climate change and environmental issues. Ideally and partly through the use of sophisticated sensors, cities will become less congested, less polluted and generally more livable.

The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

In this course you will learn about ,

– artificial intelligence for business

– artificial intelligence for finance

– ai in business

– ai technology

– ai development

– ai and business

– use of ai in business optimize business processes

– using AI minimizing costs maximizing revenues maximizing return on investment

– Artificial Intelligence to solve Business Problems

– AI intuition

– AI models

– AI career

– Business-driven Leverage AI

– AI practitionerTechnology

– technology enthusiasts

– AI driven business

Who this course is for:

  • Beginners
  • Information lovers

Course content

2 sections • 12 lectures • 52m total length

Can Machine Learning, Wearable Tech Help Treat Mental Health?

Combining modern wearable devices and machine learning capabilities assists researchers in distinguishing patient resilience and psychological health, new research shows.

Machine learning.

May 09, 2023 – New research from the Icahn School of Medicine at Mount Sinai in New York indicated that using Apple Watch data, such as heart rate variability and resting heart rate, could assist in training machine learning models to determine patient well-being and resilience.

According to the Centers for Disease Control and Prevention (CDC), over 20 percent of US adults have a mental illness. The CDC also noted that mental health diagnoses are some of the most common health conditions in the US.

This latest study showed that wearable devices could help support patients with mental health diagnoses by collecting assistive data.

Dig Deeper

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In the paper, researchers noted that resilience has a strong correlation with stress mitigation, morbidity reduction, and disease management. Although wearable devices are not intended to gather information surrounding this, passively collected wearable data may provide valuable insight when training machine learning technology.

To draw conclusions, researchers considered data from a previous study involving wearable devices to manage patient health. In the study, 329 healthcare workers wore an Apple Watch Series 4 or 5 that recognized heart rate variability and resting heart rate.

This data, along with baseline survey results, led researchers to conclude that the collected information was indicative of resilience and well-being.

Researchers noted that these positive results provided a foundation for the future use of wearable devices to determine physical and psychological health. The combination of these capabilities and machine learning developments could play a crucial role in treating various conditions, noted the researchers.

“We hope that this approach will enable us to bring psychological assessment and care to a larger population, who may not have access at this time,” said Micol Zweig, MPH, co-author of the paper and Associate Director of Clinical Research, Hasso Plattner Institute for Digital Health at Mount Sinai, in a press release. “We also intend to evaluate this technique in other patient populations to further refine the algorithm and improve its applicability.”

Similar occurrences from the past have shown that the combination of wearable device data and machine learning capabilities can assist in determining disease outcomes.

Published in March in JMIR Formative Research, another paper described how the use of an Apple Watch could predict pain scores in hospitalized sickle cell disease patients, and how this data could then be used to build machine learning algorithms to predict pain scores of vaso-occlusive crises (VOCs).

Including sickle cell disease patients admitted to Duke University SCD Day Hospital for a VOC, the study provided patients with an Apple Watch Series 3 which they wore throughout their visit. The device gathered data such as continuous heart rate, heart rate variability, and caloric information. Meanwhile, three machine learning models analyzed vital sign and pain score data from patient EMRs and were then trained using half of the Apple Watch data.

Researchers concluded that machine learning models predicted pain scores with a high level of accuracy, validating the feasibility of using wearable device data to predict pain scores during VOCs.