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YouTube
Ensembles (3): Gradient Boosting
Gradient boosting ensemble technique for regression
20-Oct-2015
YouTube
Logistic Regression
6.1 - Classification - [ Machine Learning ] By Andrew Ng
Complete Playlist: https://www.youtube.com/watch?v=1ZzckGxT76g&list=PLLH73N9cB21V_O2JqILVX557BST2cqJw4 For any query you can comment it! We try ...
13-Aug-2015
Ensemble learning - Scholarpedia

Ensemble learning : Curator: Robi Polikar
06-Apr-2015
Machine Learning Done Wrong - ML in the Valley

ML in the Valley1. Take default loss function for granted
11-Mar-2015
Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started

KDnuggetsInterview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started
02-Feb-2015
Random forests - classification description

Random Forests Leo Breiman and Adele Cutler
06-Jan-2015
0xdata - Better Machine Learning, End-to-End

0xdata, makers of H2O - The Open Source In-Memory Prediction Engine for Big Data Science
21-Oct-2014
YouTube
lda
Topic modeling and LDA.mpeg
topic modeling, LDA.
21-Oct-2014
Latent Dirichlet allocation - Wikipedia, the free encyclopedia

Latent Dirichlet allocation : In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.[1]
21-Oct-2014
Alpine Blog

Multinomial Logistic Regression With Apache Spark
14-Oct-2014
YouTube
How Random Forest algorithm works
In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
08-Sep-2014
YouTube
Lecture 23 Decision trees and neural nets
CS188 Artificial Intelligence UC Berkeley, Fall 2013 Lecture 23 Decision trees and neural nets Instructor: Prof. Pieter Abbeel.
07-Sep-2014
Matrix decomposition - Wikipedia, the free encyclopedia

Matrix decomposition : In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a factorization of a matrix into a product of matrices. There are many different matrix decompositions; each finds use among a particular class of problems.
03-Sep-2014
Support vector machine - Wikipedia, the free encyclopedia

Support vector machine : In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
03-Sep-2014
YouTube
A Gentle Introduction To Machine Learning; SciPy 2013 Presentation
Authors: Kastner, Kyle, Southwest Research Institute Track: Machine Learning This talk will be an introduction to the root concepts of machine learning, star...
03-Sep-2014
YouTube
Frequent Pattern Mining - Apriori Algorithm
Heres a step by step tutorial on how to run apriori algorithm to get the frequent item sets. Recorded this when I took Data Mining course in Northeastern Un...
22-Aug-2014
Market Basket Analysis with Mahout | DATASCIENCE HACKS

Also known as Affinity Analysis/Frequent Pattern Mining: Finding patterns in huge amounts of customer transactional data is called market basket analysis. This is useful where store's transactional data is readily available. Using market basket analysis, one can find purchasing patterns. Market basket analysis is also called associative rule mining (actually its otherway around) or affinity?
22-Aug-2014
Apache Mahout: Scalable machine learning and data mining

Mahout has a Top K Parallel FPGrowth Implementation. Its based on the paper http://infolab.stanford.edu/~echang/recsys08-69.pdf with some optimisations in mining the data.
22-Aug-2014
Data Mining Algorithms In R/Frequent Pattern Mining/The FP-Growth Algorithm - Wikibooks, open books for an open world

Data Mining Algorithms In R/Frequent Pattern Mining/The FP-Growth Algorithm
22-Aug-2014
Using Feature Selection Methods in Text Classification | Datumbox

In text classification, the feature selection is the process of selecting a specific subset of the terms of the training set and using only them in the
19-Aug-2014
Machine Learning Tutorial: The Max Entropy Text Classifier | Datumbox

In this tutorial we will discuss about Maximum Entropy text classifier, also known as MaxEnt classifier. The Max Entropy classifier is a discriminative
19-Aug-2014
Machine Learning Tutorial: The Naive Bayes Text Classifier | Datumbox

In this tutorial we will discuss about Naive Bayes text classifier. Naive Bayes is one of the simplest classifiers that one can use because of the simple
19-Aug-2014
Comparing Pattern Mining on a Billion Records with HP Vertica and Hadoop

How to mine patterns within the HP Vertica Analytics Platform. Pattern mining has many applications and can be used to discover hidden structures in data.
05-Jun-2014
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08-Apr-2014
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06-Apr-2014
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17-Mar-2014
Apache Mahout: Scalable machine learning and data mining

Copyright 2014 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache and the Apache feather logos are trademarks of The Apache Software Foundation.
07-Mar-2014
Slope One - Wikipedia, the free encyclopedia

Slope One : Slope One is a family of algorithms used for collaborative filtering, introduced in a 2005 paper by Daniel Lemire and Anna Maclachlan.[1] Arguably, it is the simplest form of non-trivial item-based collaborative filtering based on ratings. Their simplicity makes it especially easy to implement them efficiently while their accuracy is often on par with more complicated and computationally expensive algorithms.[1][2] They have also been used as building blocks to improve other algorithms.[3][4][5][6][7][8][9] They are part of major open-source libraries such as Apache Mahout and Easyrec.
07-Mar-2014
Creating an on-line recommender system with Apache Mahout | Blog of Adam Warski

Blog of Adam WarskiCreating an on-line recommender system with Apache Mahout
07-Mar-2014
YouTube


07-Mar-2014
YouTube


07-Mar-2014
Kick Start Hadoop: Mahout Recommendations with Data Sets containing Alpha Numeric Item Ids

Kick Start Hadoop : This Blog is intended to give budding MapReduce developers a start off in developing hadoop based applications. It involves some development tips and tricks on hadoop MapReduce programming, tools that use map reduce under the hood and some practical applications of hadoop using these tools. Most of the code samples provided here is tested on hadoop environment but still do post me if you find any not working.
07-Mar-2014
Salmon Run: Learning Mahout : Collaborative Filtering

Salmon Run : Swimming upstream on the technology tide, one technology at a time. A collection of articles, tips, and random musings on application development and system design.
07-Mar-2014
Machine Learning with Apache Mahout: Refining the Recommender | Dr Dobb's

Mahout components implement popular algorithms and can be unplugged easily when no longer needed.
07-Mar-2014


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06-Oct-2013
Singular value decomposition - Wikipedia, the free encyclopedia

Singular value decomposition : In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics.
01-Oct-2013
CaltechX: CS1156x: Learning From Data | edX

Learning From Data : This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:
15-Sep-2013