Thinking Data Science
A Data Science Practitioner’s Guide
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
Written for both aspiring and working data scientists to develop and improve their AI applications Teaches how to handle numeric, text and image datasets, GOFAI and ANN/DNN development, and use automated tools Includes a large section on clustering algorithms, explaining their applications for various sized datasets
Autor*in
Poornachandra Sarang
Themen in »Thinking Data Science«
AI-Assisted Data Science Responsible AI Large Language Models (LLMs) Machine Learning Workflows Reinforcement Learning from Human Feedback (RLHF) ChatGPT for Data Science Prompt Engineering for Developers Model Interpretability Ethical AI Frameworks Fairness and Bias in AI AI Regulatory Compliance (EU AI Act) Explainable AI (XAI) Production-Ready AI Data Science Workflows