What Is Machine Learning? Definition, Examples & Use Cases

What Is Machine Learning?

Machine learning (ML) lets systems identify patterns and make predictions—whether it’s sorting images, forecasting demand, or tailoring user experiences. By training statistical models on large datasets and letting them learn autonomously, ML continually improves over time. To see how this plays out across an entire organization, you can dive into the AI Business  Automation: Boost Efficiency & Drive Growth pillar.

A Brief History

The Early Days (1950s–1970s)
Early perceptron experiments laid the foundation for today’s predictive insights in the business intelligence toolkit.

Statistical Learning Era (1980s–1990s)
Techniques like decision trees and support vector machines unlocked robust classification and regression—core to modern accounting automation playbook.

Big Data & Kernel Methods (2000s)
As data volumes exploded, kernel-based methods (SVMs, Gaussian processes) became go-to tools for spotting anomalies in threat detection strategies.

Deep Learning Renaissance (2010s)
Deep neural nets delivered breakthroughs in language understanding and vision. Today they power everything from conversational agents (see the virtual agent toolkit) to AI-assisted writing (check out the copywriting accelerator guide).

Automated & Hybrid ML (2020s)
AutoML platforms and human-in-the-loop systems are now making it easier to launch pilots in areas like the customer experience roadmap.

Key Technologies

• Supervised learning: Training on labeled examples—used in demand forecasting and anomaly detection in the predictive analytics roadmap.

• Unsupervised learning: Finding structure in data for customer segmentation in the customer experience roadmap.

• Reinforcement learning: Reward-based agents optimizing routes and resource allocation in operations & logistics flows.

• Deep learning: Multi-layer networks powering speech recognition and advanced assistants in the virtual agent toolkit.

• Transfer learning: Fine-tuning pre-trained models for new tasks—speeding deployments in the copywriting accelerator guide.

How Companies Use ML Today

• Churn prediction: Combining insights from the predictive analytics roadmap to retain subscribers.

• Fraud detection: Real-time anomaly monitoring in the accounting automation playbook.

• Recommendation engines: Powering personalized offers in the customer experience roadmap.

• Process optimization: Fine-tuning warehouse workflows via insights from operations & logistics flows.

• Talent analytics: Screening and forecasting performance through the talent management manual.

• Autonomous vehicles: Guiding fleets in the inventory management blueprint.

• Dynamic pricing: Adjusting rates on the fly with the marketing automation playbook.

Benefits vs. Challenges

Benefits

• Data-driven decisions in the business intelligence toolkit

• Personalized customer journeys via the customer experience roadmap

• Efficiency gains in operations & logistics flows

• Scalable bookkeeping in the accounting automation playbook

Challenges

• Ensuring data quality and governance in the predictive analytics roadmap

• Mitigating bias and explaining model decisions

• Integrating ML into legacy systems with the it management handbook

• Closing skills gaps around advanced ML techniques

Looking Ahead

• Edge ML: Running inference on devices alongside the it management handbook.

• AutoML & democratization: Low-code platforms spreading ML beyond data science teams—see more in boost efficiency & drive growth.

• Explainable ML: Tools for regulated industries in the accounting automation playbook.

• Multimodal models: Combining text, image, and audio for richer experiences in the virtual agent toolkit.

• Sustainable AI: Optimizing compute for environmental impact—explore in boost efficiency & drive growth.

Next Steps

1. Pick a small pilot in demand forecasting or anomaly detection using the predictive analytics roadmap.

2. Grab one of the free ML notebook templates to prototype in days.

3. Plug the solution into workflows like the virtual agent toolkit or operations & logistics flows.

4. Monitor performance, retrain models, and iterate.

Further Reading

• “Machine Learning,” Wikipedia: https://en.wikipedia.org/wiki/Machine_learning

• Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997

• McKinsey & Company, “The State of Machine Learning in 2025”