FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title: Statistics and Machine Learning in Python
- Author(s) Edouard Duchesnay, Tommy Lofstedt, Feki Younes
- Publisher: HAL Science (2021)
- Hardcover: N/A
- eBook: PDF (388 pages)
- Language: English
- ISBN-10/ASIN: B0B14JM78D
- ISBN-13: 979-8828491568
- Share This:
This book illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.
About the Authors- N/A
- Statistics and Machine Learning in Python (Edouard Duchesnay, et al.)
- The Mirror Site (1) - HTML
- The Mirror Site (2) - PDF
-
Learning Statistics with Python (Ethan Weed)
This book explains basic concepts of statistics within the framework of using Python. The blending of statistics and computer coding has quickly become a standard in research to in both academia and industry.
-
Statistical Foundations of Machine Learning: The Handbook
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.
-
Bayesian Reasoning and Machine Learning (David Barber)
This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.
-
Machine Learning with Python Tutorial (Bernd Klein)
This practical guide provides helps to solve machine learning challenges you may encounter in your work. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.
-
Python Machine Learning Projects (Brian Boucheron, et al)
This book tries to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning. If you know some Python and you want to use machine learning and deep learning, pick up this book.
-
Introduction to Statistical Learning: with Applications in Python
This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
-
Statistical Learning and Sequential Prediction
This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.
-
Statistical Foundations of Machine Learning: The Handbook
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.
-
Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
-
Bayesian Methods for Hackers: Probabilistic Programming
This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.
-
Pattern Recognition and Machine Learning (Christopher Bishop)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
-
Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.
-
The Hundred-Page Machine Learning Book (Andriy Burkov)
Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.
:
|
|