Thinking Data Science: A Data Science Practitioner's Guide

This definitive guide to machine learning projects answers the questions aspiring and experienced data scientists frequently face. Are you unsure which technology to use for your ML development? Should you choose GOFAI, ANN/DNN, or transfer learning? Can you rely on AutoML for model development? What if a client provides gigabytes or terabytes of data for building analytic models? How do you handle high-frequency, dynamic datasets? This book provides practitioners with a consolidated view o...