In 2021 and beyond, machine learning (ML) will be the chief business tech resource. In fact, the International Data Corporation (IDC) estimates that investments in this disruptive technology will climb to $77.6 billion by 2022. AI-driven software is helping businesses across the globe improve productivity, efficiency, and preventative maintenance, and quite frankly, business leaders unwilling to adopt will only be left behind.
The exciting new tech discipline known as machine learning utilizes a combination of mathematics, statistics, and artificial intelligence to foster a data-driven system development paradigm. Data models, data analysis, and feedback are leveraged to define and refine algorithms to continuously improve the model's accuracy and results. By predicting data patterns, machine learning allows engineers to go further than just writing a program that carries out a specific task or set of tasks—algorithms are written for the purpose of teaching a computer to write its own programs.
Understanding the Most Appropriate ML Applications
Although new, exciting, and unquestionably revolutionary, there's no one-size-fits-all approach to machine learning. As a business or technology leader, you're likely familiar with the fact that certain approaches and solutions are suitable for certain classes of problems. So how can machine learning be used?
Common scenarios for sourcing ML to address the human issue of poor data literacy:
- Logical rules are either insufficient or totally unavailable to describe an environment, but one can deduce actionable rules
- The data is problematic for typical analytic processes
- The outcome's accuracy is more important than understanding why an outcome is suggested
- The next actions are varied, and the most appropriate action depends on conditions that cannot be noted in advance
Currently, machine learning spans various industries, offering competitive advantages to businesses savvy enough to get ahead. ML's capability to understand natural human language has helped HR teams filter applications and has refined the recruiting process, supported marketing and sales teams in developing algorithms for paid advertising and nurture campaigns, and is even leveraged in agriculture for crop yield predictions.
You can influence future innovations in data science by following your passion for tech today. Tulane School of Professional Advancement's renowned Information Technology program is designed for working adults no matter their background in tech: start out learning the fundamentals with our graduate certificates or advance your current knowledge by pursuing a master's degree in Cybersecurity or IT Management. Discover what's possible by requesting more information about our program.
