Technology Landscape & Tools for Data Management

Machine Learning    |    Intermediate
  • 9 videos | 26m 27s
  • Includes Assessment
  • Earns a Badge
Likes 22 Likes 22
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.

WHAT YOU WILL LEARN

  • describe the concept and characteristics of the current technology landscape from the data perspective as well as the tools involved
    describe the comparative benefits of essential data management tools
    recognize the need for machine learning in modern data analytics
    list the various prominent tools and frameworks that can be used to implement machine learning
  • work with scikit-learn to implement machine learning
    recognize the capabilities provided by Python and R in the data management cycle
    specify the capabilities and benefits provided by the implementation of machine learning in the cloud
    explore essential data management tools and implement machine learning with scikit-learn

IN THIS COURSE

  • Playable
    1.  Course Overview
    1m 35s
  • Playable
    2.  Technology Landscape and Tools
    4m 41s
    After completing this video, you will be able to describe the concept and characteristics of the current technology landscape from a data perspective as well as the tools involved. FREE ACCESS
  • Locked
    3.  Tool Comparison
    3m 3s
    Upon completion of this video, you will be able to describe the benefits of essential data management tools in comparison to each other. FREE ACCESS
  • Locked
    4.  Machine Learning in Data Analytics
    3m
    Upon completion of this video, you will be able to recognize the need for machine learning in modern data analytics. FREE ACCESS
  • Locked
    5.  Machine Learning Tools
    2m 51s
    Upon completion of this video, you will be able to list the various prominent tools and frameworks for implementing machine learning. FREE ACCESS
  • Locked
    6.  Machine Learning Implementation
    2m 51s
    Find out how to work with scikit-learn to implement machine learning. FREE ACCESS
  • Locked
    7.  Python and R for Data Management
    3m 57s
    Upon completion of this video, you will be able to recognize the capabilities provided by Python and R in the data management cycle. FREE ACCESS
  • Locked
    8.  Cloud and Machine Learning
    3m 2s
    After completing this video, you will be able to specify the capabilities and benefits provided by machine learning in the cloud. FREE ACCESS
  • Locked
    9.  Exercise: Implement Machine Learning on Scikit-learn
    1m 27s
    In this video, you will learn how to explore essential data management tools and implement machine learning with scikit-learn. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

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