An Introduction to Machine Learning, 2nd Edition
This book presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Download free tutorial in PDF (348 pages) created by Miroslav Kubat .
|File type :||HTML|
Take advantage of this course called An Introduction to Machine Learning, 2nd Edition to improve your Others skills and better understand Machine Learning.
This course is adapted to your level as well as all Machine Learning pdf courses to better enrich your knowledge.
All you need to do is download the training document, open it and start learning Machine Learning for free.
This tutorial has been prepared for the beginners to help them understand basic Machine Learning Others. After completing this tutorial you will find yourself at a moderate level of expertise in Machine Learning from where you can take yourself to next levels.
This tutorial is designed for Machine Learning students who are completely unaware of Machine Learning concepts but they have basic understanding on Others training.
- Information Technology for Management, 7th Edition (Type: PDF, Size: PDF, 754 pages, 17.7 MB, Downloads: 24)
- Introduction to Financial Mathematics: Concepts and Computational Methods (Type: PDF, Size: PDF (290 pages, 3.10 MB), Downloads: 6)
- Programming on Parallel Machines: GPU, Multicore, Clusters and More (Type: PDF, Size: Multiple formats: PDF (410 page, 2.55 MB), ePUB, Kindle, Text, etc., Downloads: 12)
- Internet of Things (IoT) in 5 Days: an easy guide to Wireless Sensor Networks (WSN), IPv6, and IoT (Type: PDF, Size: PDF (227 pages, 19.9 MB), ePub, Mobi (Kindle), etc., Downloads: 47)
- MATLAB Quick Guide (Type: PDF, Size: 349.80 Kb, Downloads: 269)
- Reinforcement Learning: An Introduction, Second Edition (Type: PDF, Size: : PDF (548 pages), Downloads: 29)
- Understanding Machine Learning: From Theory to Algorithms (Type: PDF, Size: 2540.539 Kb, Downloads: 259)
- Machine Learning for Cyber Physical Systems (Type: PDF, Size: PDF, Downloads: 10)
- Overview of Machine Learning (Type: PDF, Size: 4460.385 Kb, Downloads: 117)
- Machine Learning with TensorFlow (Type: PDF, Size: HTML, Downloads: 8)