This Skillsoft Aspire course explores various tools you can utilize to get better data analytics for your organization. You will learn the important factors to consider when selecting tools, velocity, the rate of incoming data, volume, the storage capacity or medium, and the diversified nature of data in different formats. This course discusses the various tools available to provide the capability of implementing machine learning, deep learning, and to provide AI capabilities for better data analytics. The following tools are discussed: TensorFlow, Theano, Torch, Caffe, Microsoft cognitive tool, OpenAI, DMTK from Microsoft, Apache SINGA, FeatureFu, DL4J from Java, Neon, and Chainer. You will learn to use SCIKIT-learn, a machine learning library for Python, to implement machine learning, and how to use machine learning in data analytics. This course covers how to recognize the capabilities provided by Python and R in the data management cycle. Learners will explore Python; the libraries NumPy, SciPy, Pandas to manage data structures; and StatsModels. Finally, you will examine the capabilities of machine learning implementation in the cloud.
Data Tools: Technology Landscape & Tools for Data Management