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  • - The Ultimate Beginners Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step-By-Step
    av Mark Reed
    296 - 360,-

  • - Study Deep Learning Through Data Science. How to Build Artificial Intelligence Through Concepts of Statistics, Algorithms, Analysis and Data Mining
    av Samuel Hack
    260,-

  • - A Math Guide to Mastering Deep Learning and Business Application. Understand How Artificial Intelligence, Data Science, and Neural Networks Work Through Real Examples
    av Samuel Hack
    260,-

  • - Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
    av Mg Martin
    250,-

  • - Discover the Essentials of Machine Learning, Data Analysis, Data Science, Data Mining and Artificial Intelligence Using Python Code with Python Tricks
    av Samuel Hack
    260,-

  • - From Introductory concepts to Machine Learning Models
    av Editor Ijsmi
    400,-

  • - A Probabilistic Perspective
    av Kevin P. Murphy
    1 246,-

    A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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