This chapter, as it belongs in a broader theme of practices and principles for data science, elucidates the mapping strategy of a given downside assertion to a quantified assertion driven by information. The chapter focuses on aspects of machine learning algorithms, applications, and practices. Machine studying approaches lined here, include discriminative and generative modeling methodologies similar to supervised, unsupervised, and deep studying algorithms. The data characterization subjects embrace practices on handling lacking values, resolving class imbalance, vector encoding, and information transformations.
