**What is ****Mlpack**** Library?**

**mlpack** is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users.

This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.

As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.

mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)

mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.

mlpack bindings for R are provided by the RcppMLPACK project.

Currently mlpack supports the following algorithms:

- Collaborative Filtering
- Decision stumps (one-level decision trees)
- Density Estimation Trees
- Euclidean Minimum Spanning Trees
- Gaussian Mixture Models (GMMs)
- Hidden Markov Models (HMMs)
- Kernel Principal Component Analysis (KPCA)
- K-Means Clustering
- Least-Angle Regression (LARS/LASSO)
- Linear Regression
- Local Coordinate Coding
- Locality-Sensitive Hashing (LSH)
- Logistic regression
- Max-Kernel Search
- Naive Bayes Classifier
- Nearest neighbor search with dual-tree algorithms
- Neighbourhood Components Analysis (NCA)
- Non-negative Matrix Factorization (NMF)
- Principal Components Analysis (PCA)
- Independent component analysis (ICA)
- Rank-Approximate Nearest Neighbor (RANN)
- Simple Least-Squares Linear Regression (and Ridge Regression)
- Sparse Coding, Sparse dictionary learning

For more detail visit here - http://mlpack.org/docs.html

**Video for Mlpack**

https://www.youtube.com/watch?v=yQtp3gf5wtY