» » Intro Machine Learng Pb

Download Intro Machine Learng Pb fb2

by Kodratoff
Download Intro Machine Learng Pb fb2
Computer Science
  • Author:
  • ISBN:
  • ISBN13:
  • Genre:
  • Publisher:
    Univ Coll Londo (December 1, 1988)
  • Pages:
    304 pages
  • Subcategory:
    Computer Science
  • Language:
  • FB2 format
    1630 kb
  • ePUB format
    1491 kb
  • DJVU format
    1590 kb
  • Rating:
  • Votes:
  • Formats:
    azw docx docx lrf

Want to be notified of new releases in Prooffreader/intro machine learning? . Introduction to Machine Learning. From 2015, a series of Jupyter Notebooks and accompanying slideshow and video.

Introduction to Machine Learning.

Introducción a Machine Learning. En este repositorio hay notebooks de Jupyter para mi clase de Introducción a Machine Learning en la Universidad del Desarrollo. Como material utilizo los notebooks creados por Jake Vanderplas en su libro Python Data Science Handbook, traducidos al castellano, y en algunos casos, con datos chilenos que permiten entender mejor el contexto de los algoritmos.

No previous knowledge of pattern recognition or machine learning concepts is assumed.

Fork 0. fedegott/Intro Machine Learning. Intro Machine Learning/Intro ML 3. Fetching contributors.

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects. This program is intended for students with experience in Python, who have not yet studied Machine Learning topics. Learn foundational machine learning techniques - from data manipulation to unsupervised and supervised algorithms. 3 months to complete.

This is a description of the state of the art in machine learning (ML) which includes many exercises and examples designed to give the reader a firm grasp of the concepts and techniques of this expanding subject. Although it develops the necessary theoretical basis of ML, the book begins with an overview suitable for readers lacking a theoretical background. The technical concepts of ML are illustrated using Prolog and the book shows how some of the complex problems inherent in using logic programming as knowledge representation may be handled. In addition, it contains the results of a number of leading ML researchers such as Mitchell, Michalski and Winston which are explored and presented for study. The work is intended to be a good starting point from which to learn about ML and should be particularly suitable for undergraduates taking computer science courses but also for those in the first year of an MSc or PhD course.