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F. Firouzi and B. Farahani
drops features. Almost all kinds of data can be connected and effectively
analyzed – from a small amount of data like a spreadsheet to big data like
Hadoop. Note that Tableau is considered as a business intelligence framework
rather than a holistic machine learning platform. For example, users cannot
develop cutting-edge predictive maintenance solutions in Tableau.
• Microsoft Power BI: Microsoft Power BI is a well-known business intelligence
framework on the market, meant to analyze data and share and visualize
subsequent data.
• QlikView: QlikView is a well-known option in the data analytics domain, and
the QlikView tool is one of Tableau’s closest competitors. It offers effective data
visualization, business intelligence, and enterprise reporting options.
4.8.3 Advanced Data Analytical and Machine Learning
Frameworks
• Scikit-learn: Scikit-learn, an open-source library of machine learning algorithms,
is widely used and well documented. It seeks to provide commonly used
algorithms for Python users. It is rapidly becoming the go-to platform for
machine learning and is continuously evolving to provide greater efficiency,
speed, and big data capabilities. Scikit-learn is commonly utilized with smaller
data, but does provide a useful group of algorithms for clustering, out-of-core
classification, decomposition, and regression.
• TensorFlow: TensorFlow, an open-source library of software useful for executing
numerical calculations with data flow graphs, was recently created by Google.
Many would argue that it is the best framework for deep learning and it has been
utilized by top-tier organizations including Twitter, IBM, and Airbus because it
is engineered based on a modular architecture resulting in significant flexibility.
The most widely known TensorFlow use case is Google Translate which
combines multiple functions such as forecasting; natural language processing;
image, speech, and handwriting recognition; tagging; and text classification.
• Caffe: Caffe is another platform for deep learning that supports command-line
interfaces as well as other interfaces like MATLAB, C++, Python, and C. It is
widely recognized for its ability to model convolution neural networks (CNN)
as well its speed and transposability. The greatest benefit of Caffe is that it
has a large repository of networks that have been pre-trained and are ready for
immediate use.
• RapidMiner: RapidMiner is predominantly concerned with speed in achieving
data insights in complicated data science. The visualization interface includes
ready-to-use workflows, machine learning elements, and data connectivity.
RapidMiner can be integrated with several technologies such as Python and R. It
is also capable of automating many tasks such as the selection of models, what-if
gaming, data preparation, and predictive modeling.
F. Firouzi and B. Farahani
drops features. Almost all kinds of data can be connected and effectively
analyzed – from a small amount of data like a spreadsheet to big data like
Hadoop. Note that Tableau is considered as a business intelligence framework
rather than a holistic machine learning platform. For example, users cannot
develop cutting-edge predictive maintenance solutions in Tableau.
• Microsoft Power BI: Microsoft Power BI is a well-known business intelligence
framework on the market, meant to analyze data and share and visualize
subsequent data.
• QlikView: QlikView is a well-known option in the data analytics domain, and
the QlikView tool is one of Tableau’s closest competitors. It offers effective data
visualization, business intelligence, and enterprise reporting options.
4.8.3 Advanced Data Analytical and Machine Learning
Frameworks
• Scikit-learn: Scikit-learn, an open-source library of machine learning algorithms,
is widely used and well documented. It seeks to provide commonly used
algorithms for Python users. It is rapidly becoming the go-to platform for
machine learning and is continuously evolving to provide greater efficiency,
speed, and big data capabilities. Scikit-learn is commonly utilized with smaller
data, but does provide a useful group of algorithms for clustering, out-of-core
classification, decomposition, and regression.
• TensorFlow: TensorFlow, an open-source library of software useful for executing
numerical calculations with data flow graphs, was recently created by Google.
Many would argue that it is the best framework for deep learning and it has been
utilized by top-tier organizations including Twitter, IBM, and Airbus because it
is engineered based on a modular architecture resulting in significant flexibility.
The most widely known TensorFlow use case is Google Translate which
combines multiple functions such as forecasting; natural language processing;
image, speech, and handwriting recognition; tagging; and text classification.
• Caffe: Caffe is another platform for deep learning that supports command-line
interfaces as well as other interfaces like MATLAB, C++, Python, and C. It is
widely recognized for its ability to model convolution neural networks (CNN)
as well its speed and transposability. The greatest benefit of Caffe is that it
has a large repository of networks that have been pre-trained and are ready for
immediate use.
• RapidMiner: RapidMiner is predominantly concerned with speed in achieving
data insights in complicated data science. The visualization interface includes
ready-to-use workflows, machine learning elements, and data connectivity.
RapidMiner can be integrated with several technologies such as Python and R. It
is also capable of automating many tasks such as the selection of models, what-if
gaming, data preparation, and predictive modeling.