Thinking data science: a data science practitioner’s guide
- Cham Springer 2024
- xx, 358 p.
- The Springer Series in Applied Machine Learning (SSAML) .
Table of contents: Front Matter Pages i-xx Download chapter PDF Data Science Process Poornachandra Sarang Pages 1-18 Dimensionality Reduction Poornachandra Sarang Pages 19-52 Part I Front Matter Pages 53-54 Download chapter PDF Regression Analysis Poornachandra Sarang Pages 55-73 Decision Tree Poornachandra Sarang Pages 75-96 Ensemble: Bagging and Boosting Poornachandra Sarang Pages 97-129 K-Nearest Neighbors Poornachandra Sarang Pages 131-141 Naive Bayes Poornachandra Sarang Pages 143-152 Support Vector Machines Poornachandra Sarang Pages 153-165 Part II Front Matter Pages 167-170 Download chapter PDF Centroid-Based Clustering Poornachandra Sarang Pages 171-183 Connectivity-Based Clustering Poornachandra Sarang Pages 185-195 Gaussian Mixture Model Poornachandra Sarang Pages 197-207 Density-Based Clustering Poornachandra Sarang Pages 209-228 BIRCH Poornachandra Sarang Pages 229-236 CLARANS Poornachandra Sarang Pages 237-242 Affinity Propagation Clustering Poornachandra Sarang Pages 243-250 STING & CLIQUE Poornachandra Sarang Pages 251-257 Part III Front Matter Pages 259-259
This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.
The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.