Showing results for "Driven" in Computer Science

The difference between growing your business and going out of business is your ability to think strategically. The problem is, most leaders are stuck in the operational weeds, struggling to find the time to make better strategic decisions. This challenge is only heightened by the rise of AI. While you know AI is the future, current demands leave you with no time to explore its benefits. Meanwhile, your competition is gaining an edge by integrating AI. The time to act is now.

Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems.

As humans compete for work against faster, cheaper, and smarter technology, the flood of AI-generated information undermines traditional expertise, eroding essential skills like writing, analysis, and decision-making. Despite these challenges, AI presents immense opportunities for those who master its intricacies, such as driving innovation, improving efficiency, and creating new markets. Can we thread the needle balancing the immeasurable benefits with the profound risks that AI presents to our future?