Here’re 3 Top Tools to Practice Machine Learning

Here’re three top tools for clustering, supervised classification, Bayesian networks, and dynamic environments. Machine learning useful in creating algorithms for Robots.

Journey – Industrialization to Machine Learning

Technology is fourth industrial revolution.
1. Industrial
2. Automation
3. Semi-Automation
4. IoT and Machine Learning

Tools for Machine Learning

1). Scikit-learn (Pedregosa et al., 2011)

is a free Python package. It has a command-line interface and requires some Python programming skills. One of its main strong points is its well-written online documentation.

2). KNIME (Berthold et al., 2008)

is the acronym of Konstanz Information Miner and is developed and maintained by a Swiss company based on initial developments at the University of Konstanz, Germany. KNIME is open-source, although commercial licenses exist for companies requiring professional technical support. One of the greatest strengths of KNIME is its integration with WEKA and R.

RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

3). RapidMiner 

(Hofmann and Klinkenberg, 2013) is a Java-based tool currently under development by the company RapidMiner, Germany. Previous versions were open source. RapidMiner offers an integrating environment with a visually appealing and user-friendly GUI. RapidMiner also offers the option of application wizards that construct the machine learning process automatically based on the required project goals.

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Author: Srini

Experienced software developer. Skills in Development, Coding, Testing and Debugging. Good Data analytic skills (Data Warehousing and BI). Also skills in Mainframe.