Description

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 of the entire data science process in a single "cheat sheet."

The core challenge for a data scientist is to extract meaningful information from huge datasets to create better strategies for businesses. Many machine learning algorithms and neural networks are designed to perform analytics on such datasets. For a data scientist, choosing the most suitable algorithm for a given dataset can be a daunting decision. Although there is no single answer, a systematic approach to problem solving is essential. This book describes a range of ML algorithms conceptually and discusses a structured process for selecting ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key focus of this book. Thinking Data Science will help practising data scientists, academics, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data is small or big.