Machine Learning (ml) Has Become A Commonplace Element In Our Everyday Lives And Astandard Tool For Many Fields Of Science And Engineering. To Make Optimal Use Of Ml, It Isessential To Understand Its Underlying Principles.this Book Approaches Ml As The Computational Implementation Of The Scientific Principle.this Principle Consists Of Continuously Adapting A Model Of A Given Data-generatingphenomenon By Minimizing Some Form Of Loss Incurred By Its Predictions.the Book Trains Readers To Break Down Various Ml Applications And Methods In Terms Ofdata, Model, And Loss, Thus Helping Them To Choose From The Vast Range Of Ready-made Ml Methods.the Book's Three-component Approach To Ml Provides Uniform Coverage Of A Wide Range Ofconcepts And Techniques. As A Case In Point, Techniques For Regularization, Privacy-preservationas Well As Explainability Amount To Specific Design Choices For The Model, Data, And Loss Of A Ml Method.