Since the first edition of "Data Science and Analytics with Python" we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This has led to book becoming a key textbook among practitioners and students.
Since the first edition of "Data Science and Analytics with Python" we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This has led to book becoming a key textbook among practitioners and students.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr Jesús Rogel-Salazar is a Lead Data Scientist with experience in the field working for companies such as The Ortus Group, TympaHealth, Barclays Bank, AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics, and the Business School at Imperial College London, UK and a a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant and data scientist in the financial industry since 2006. He is the author of the book Essential Matlab and Octave and the companion book to this volume Advanced Data Science and Analytics with Python and Statistics and Data Visualisation with Python, also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism, telematics, fintech and healthtech.
Inhaltsangabe
1. Trials and Tribulations of a Data Scientist 2. Python: For Something Completely Different 3. The Machine that Goes "Ping": Machine Learning and Pattern Recognition 4. The Relationship Conundrum: Regression 5. Jackalopes and Hares: Clustering 6. Unicorns and Horses: Classification 7. Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensemble Techniques 8. Less is More: Dimensionality Reduction 9. Kernel Tricks up the Sleeve: Support Vector Machines Appendix. Pipelines in Scikit-Learn Bibliography Index
1. Trials and Tribulations of a Data Scientist 2. Python: For Something Completely Different 3. The Machine that Goes "Ping": Machine Learning and Pattern Recognition 4. The Relationship Conundrum: Regression 5. Jackalopes and Hares: Clustering 6. Unicorns and Horses: Classification 7. Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensemble Techniques 8. Less is More: Dimensionality Reduction 9. Kernel Tricks up the Sleeve: Support Vector Machines Appendix. Pipelines in Scikit-Learn Bibliography Index
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