Data Tools: Machine Learning & Deep Learning in the Cloud

Data Tools: Machine Learning & Deep Learning in the Cloud

• Course Overview
• recognize the capabilities of Microsoft machine learning tools
• recognize the machine learning tools provided by AWS for data analysis
• specify Spark's machine leaning capabilities and the features of PySpark
• list frameworks that can be used to implement deep learning such as Keras, TensorFlow, Caffe, and PyTorch
• implement deep learning using Keras
• list tools that can be used to implement data mining and analytics and their features
• demonstrate the capabilities of building and processing data pipeline with Knime
• set up Keras, implement a deep learning algorithm, and build data pipelines using KNIME

This Skillsoft Aspire course explores the machine learning solutions provided by AWS (Amazon Web Services) and Microsoft, and how to compare the tools and frameworks that can be used to implement machine learning, and deep learning. You will learn to become efficient in data wrangling by building a foundation with data tools and technology. This course explores Machine Learning Toolkit provided by Microsoft, which provides various algorithms and applies artificial intelligence and deep learning. Learners will also examine Distributed Machine Learning Toolkit, which is hosted on Azure. Next, explore the machine learning tools provided by AWS, including DeepRacer and DeepLens which provide deep learning capabilities. You will learn how to use Amazon SageMaker, and how Jupyter notebooks are used to test machine learning algorithms. You will learn about other AWS tools, including TensorFlow, Apache MXNet, and Deep Learning AMI. Finally, learn about different tools for data mining and analytics, and how to build and process a data pipeline provided by KNIME (Konstanz Information Miner).

Data Tools: Technology Landscape & Tools for Data Management

Data Tools: Technology Landscape & Tools for Data Management

• Course Overview
• 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

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.