『機器學習』 Machine learning 是什麼？ Aurélien Géron 如是說︰

─── Hands-on Machine Learning with Scikit-Learn and TensorFlow Ch.1

這樣言簡意賅！若不整章前後文本通讀多遍，恐怕難了其意指哩？

考之以維基百科詞條，這麼講︰

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data“, in order to make predictions or decisions without being explicitly programmed to perform the task.^[1]^[2]^:2 Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.^[3]^[4] In its application across business problems, machine learning is also referred to as predictive analytics.

─── Wikipedia Machine learning

或許多了些領會！

再參閱 Adam Geitgey 的解釋︰

What is machine learning?

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic.