Naïve Bayes in Python The Naive Bayes Algorithm The naïve Bayes algorithm is a classifier based on Bayes' theorem. It relies on independence between features, which sometimes necessitates pre-processing (for example, via eigenvalue decomposition). Formally, the algorithm operates under supervised learning.
Meet the algorithm that can learn “everything about anything” The most recent advances in artificial intelligence research are pretty staggering, thanks in part to the abundance of data available on the web. We’ve covered how deep learning is helping create self-teaching and highly accurate systems for tasks such as sentiment analysis and facial recognition, but there are also models that can solve geometry and algebra problems, predict whether a stack of dishes is likely to fall over and (from the team behind Google’s word2vec) understand entire paragraphs of text. (Hat tip to frequent commenter Oneasum for pointing out all these projects.) One of the more interesting projects is a system called LEVAN, which is short for Learn EVerything about ANything and was created by a group of researchers out of the Allen Institute for Artificial Intelligence and the University of Washington. One of them, Carlos Guestrin, is also co-founder and CEO of a data science startup called GraphLab.
Computer Networking : Principles, Protocols and Practice Computer Networking : Principles, Protocols and Practice (aka CNP3) is an ongoing effort to develop an open-source networking textbook that could be used for an in-depth undergraduate or graduate networking courses. The first edition of the textbook used the top-down approach initially proposed by Jim Kurose and Keith Ross for their Computer Networks textbook published by Addison Wesley. CNP3 is distributed under a creative commons license. The second edition takes a different approach. The new features of the second edition are : The second edition of the ebook is now divided in two main parts The first part of the ebook uses a bottom-up approach and focuses on the principles of the computer networks without entering into protocol and practical details.
marioai - Mario AI Benchmark. AI and Machine Learning Experiments based on Super Mario Bros. Experiments in applying evolutionary algorithms, neural networks and other AI/CI/ML algorithms to Super Mario Bros. MarioAI is a benchmark for machine learning and artificial intelligence based on Super Mario Bros. Check out the running Mario AI Championship 2010 at ! NEW: Turing test track on CIG 2012 Coming! TextBlob: Simplified Text Processing — TextBlob 0.6.0 documentation Release v0.8.4. (Changelog) TextBlob is a Python (2 and 3) library for processing textual data. Naive Bayes Classifier From Scratch in Python The Naive Bayes algorithm is simple and effective and should be one of the first methods you try on a classification problem. In this tutorial you are going to learn about the Naive Bayes algorithm including how it works and how to implement it from scratch in Python. Update: Check out the follow-up on tips for using the naive bayes algorithm titled: “Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm“.Update March/2018: Added alternate link to download the dataset as the original appears to have been taken down. Naive Bayes ClassifierPhoto by Matt Buck, some rights reserved About Naive Bayes
Twitter Data Analytics Published by Springer Shamanth Kumar, Fred Morstatter, and Huan Liu Data Mining and Machine Learning Lab School of Computing, Informatics, and Decision Systems Engineering Arizona State University Social media has become a major platform for information sharing. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build realworld applications. This book takes a reader through the process of harnessing Twitter data to find answers to intriguing questions.
7 Major Players In Free Online Education By Jennifer Berry Imagine a world where free, college-level education was available to almost everyone. Believe it or not, you're living in that world right now. Online education has been around for decades, but in the past couple of years, interest has spiked for massive open online courses, otherwise known as MOOCs, according to Brian Whitmer, co-founder of Instructure, an education technology company that created the Canvas Network, a platform for open online courses. "Since 2012, MOOCs have caught the attention of the educational world due to their potential to disrupt how education is delivered and open up access to anyone with an Internet connection," Whitmer explains.
neural network artificial intelligence java simulation software development en - Source Codes Search Engine - HackChina sponser links: neural network artificial intelligence java simulation software development environment System modeling and identification based on BP neural network prediction and simulation of MATLAB programs (Graph - Matlab) - Based on BP neural network on with noise of second-order sys ... Pupil Pupil is an eye tracking hardware and software platform that started as a thesis project at MIT. Pupil is a project in active, community driven development. For noncommercial use, the hardware is accessible, hackable, and affordable. The software is open source and written in Python and C where speed is an issue.
Understanding Naive Bayes Classifier from scratch : Python code – Machine Learning in Action The Naive Bayes classifier is a frequently encountered term in the blog posts here; it has been used in the previous articles for building an email spam filter and for performing sentiment analysis on movie reviews. Thus a post explaining its working has been long overdue. Despite being a fairly simple classifier with oversimplified assumptions, it works quite well in numerous applications. Let us now try to unravel its working and understand what makes this family of classifiers so popular. We begin by refreshing our understanding about the fundamental concepts behind the classifier – conditional probability and the Bayes’ theorem. This is followed by an elementary example to show the various calculations which are made to arrive at the classification output.