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: machine learning in Python — scikit-learn 0.21.2 documentation

: machine learning in Python — scikit-learn 0.21.2 documentation
"We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "For these tasks, we relied on the excellent scikit-learn package for Python." "The great benefit of scikit-learn is its fast learning curve [...]" "It allows us to do AWesome stuff we would not otherwise accomplish" "scikit-learn makes doing advanced analysis in Python accessible to anyone."

http://scikit-learn.org/stable/index.html

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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.

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. COC131 Data Mining, Tuotorials Weka "The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. WEKA's users are ML researchers and industrial scientists, but it is also widely used for teaching." Tutorial 01 (13/02/09)

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

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.

Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable. Octave is distributed under the terms of the GNU General Public License.

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.

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