Algorithms Machine Learning

Machine Learning: An Introduction to Supervised and Unsupervised Learning Algorithms

Machine Learning: An Introduction to Supervised and Unsupervised Learning Algorithms
English | 2017 | ASIN: B0743826F1 | 45 pages | AZW3 | 0.2 Mb

Machine Learning for Hackers (Repost)  eBooks & eLearning

Posted by bookwarrior at Dec. 19, 2015
Machine Learning for Hackers (Repost)

Machine Learning for Hackers By Drew Conway, John Myles White
2012 | 324 Pages | ISBN: 1449303714 | EPUB + PDF | 16 MB + 23 MB

Machine Learning for Hackers  eBooks & eLearning

Posted by bookwyrm at March 12, 2012
Machine Learning for Hackers

Machine Learning for Hackers By Drew Conway, John Myles White
Publisher: O'R||eil||ly Me||dia 2012 | 322 Pages | ISBN: 1449303714 | EPUB + PDF | 16 MB + 23 MB

Machine Learning and Security: Protecting Systems with Data and Algorithms  eBooks & eLearning

Posted by IrGens at June 23, 2018
Machine Learning and Security: Protecting Systems with Data and Algorithms

Machine Learning and Security: Protecting Systems with Data and Algorithms by Clarence Chio, David Freeman
English | February 17, 2018 | ISBN: 1491979909 | True PDF | 386 pages | 6.4 MB
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python

Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python (Artificial Intelligence Book 2) Kindle Edition by Rudolph Russell
English | 2018 | ISBN: 1719528403 | 106 Pages | EPUB | 1.30 MB

Mastering Machine Learning Algorithms  eBooks & eLearning

Posted by Grev27 at June 16, 2018
Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models by Giuseppe Bonaccorso
English | 25 May 2018 | ISBN: 1788621115 | 576 Pages | EPUB | 94.35 MB

Machine Learning and Security: Protecting Systems with Data and Algorithms  eBooks & eLearning

Posted by ksveta6 at June 12, 2018
Machine Learning and Security: Protecting Systems with Data and Algorithms

Machine Learning and Security: Protecting Systems with Data and Algorithms by Clarence Chio, David Freeman
2018 | ISBN: 1491979909 | English | 386 pages | True EPUB | 9 MB

Machine Learning with C++ : Choosing the Right Algorithm  eBooks & eLearning

Posted by naag at June 9, 2018
Machine Learning with C++ : Choosing the Right Algorithm

Machine Learning with C++ : Choosing the Right Algorithm
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 274 MB
Genre: eLearning | Language: English

Introduction to Machine Learning for Data Science  eBooks & eLearning

Posted by naag at June 8, 2018
Introduction to Machine Learning for Data Science

Introduction to Machine Learning for Data Science
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | Lec: 41 | 2.03 GB
Genre: eLearning | Language: English

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.

Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent  eBooks & eLearning

Posted by AvaxGenius at June 7, 2018
Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent by Jianlong Zhou
English | PDF,EPUB | 2018 | 485 Pages | ISBN : 3319904027 | 20.83 MB

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.