Why Applied Machine Learning Is Hard - Machine Learning Mastery

Applied machine learning is challenging. You must make many decisions where there is no known "right answer" for your specific problem, such as: This is challenging for beginners that expect that you can calculate or be told what data to use or how to best configure an algorithm. In this post, you will discover the intractable nature of designing learning systems and how to deal with it. This post is divided into 6 sections inspired by chapter 1 of Tom Mitchell's excellent 1997 book Machine Learning; they are: We can define a general learning task in the field of applied machine learning as a program that learns from experience on some task against a specific performance measure. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. We take this as a general definition for the types of learning tasks that we may be interested in for applied machine learning such as predictive modeling.