From Concept to Reality: Utilizing Machine Learning for Real-World Results - PROPRIUS

Machine learning almost sounds too good to be true, and this is because the science is deceptively complex. You would think that once a machine takes in enough information and enough variables, it will be able to implement your model flawlessly. However, this is not always the case. To facilitate practical and practicable machine learning models, you must learn right alongside your computer and fine-tune your datasets to best reflect the reality you wish to achieve. Here are a handful of tips to help your engineers and researchers go from a conceptual model to the applicable real-world results.