8. ML finds applications in fraud detection, recommendation systems, image recognition, and more.
9. Deep learning, a subset of ML, revolutionizes speech recognition and image classification.
10. High-quality, diverse datasets are essential for effective ML model training.
11. Transfer learning improves ML performance by leveraging knowledge from related models.
12. Addressing bias in training data is crucial to avoid biases in ML models.
13. ML algorithms require significant computational resources and benefit from specialized hardware like GPUs and TPUs.
14. Interpretability of ML models is an ongoing area of research for transparency and accountability.
15. The future of ML holds promise with developments in explainable AI, federated learning, and ethical AI.