Machine Learning For Dummies

Chapter 22

Ten Machine Learning Packages to Master

IN THIS CHAPTER

Analyzing live-streaming data using Cloudera Oryx

Recognizing objects in images using CUDA-Convnet

Adding image recognition to web-based apps using ConvNetJS

Obtaining an R SVM implementation using e1071

Adding R GBM optimization support using gbm

Performing natural language interface tasks using Gensim

Creating a generalized linear model using glmnet

Growing a forest of R decision trees using randomForest

Performing scientific tasks of all sorts in Python using SciPy

Obtaining GBDT, GBRT, and GBM support for a variety of languages with XGBoost

The book provides you with a wealth of information about specific machine learning packages such as caret (R) and NumPy (Python). Of course, these are good, versatile packages you can use to begin your machine learning journey. It’s important to have more than a few tools in your toolbox, which is where the suggestions found in this chapter come into play. These packages provide you with additional machine learning insights and capabilities. Even though there are many other packages available on the market, these packages will give you some ideas of where to go next and make it easier to explore other packages as well.

Cloudera Oryx

http://www.cloudera.com/

Cloudera Oryx is a machine learning project for Apache Hadoop (http://hadoop.apache.org/) that provides you with a basis for performing machine learning tasks. It emphasizes the use of live data streaming. This product ...