Machine Learning & Deep Learning Tools in the Cloud
Machine Learning
| Intermediate
- 9 videos | 22m 9s
- Includes Assessment
- Earns a Badge
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).
WHAT YOU WILL LEARN
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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
IN THIS COURSE
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1m 21s
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3m 8sUpon completion of this video, you will be able to recognize the capabilities of Microsoft machine learning tools. FREE ACCESS
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3. AWS and Machine Learning2m 30sUpon completion of this video, you will be able to recognize the machine learning tools provided by AWS for data analysis. FREE ACCESS
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4. Spark Machine Learning Capabilities2m 15sAfter completing this video, you will be able to specify Spark's machine learning capabilities and the features of PySpark. FREE ACCESS
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5. Deep Learning Frameworks1m 26sAfter completing this video, you will be able to list frameworks that can be used to implement deep learning, such as Keras, TensorFlow, Caffe, and PyTorch. FREE ACCESS
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6. Deep Learning Implementation3m 52sLearn how to implement deep learning using the Keras library. FREE ACCESS
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7. Data Mining and Analytical Tools2m 45sAfter completing this video, you will be able to list tools that can be used to implement data mining and analytics, as well as their features. FREE ACCESS
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8. KNIME Capabilities3m 1sIn this video, learn how to build and process a data pipeline with Knime. FREE ACCESS
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9. Exercise: Implement Deep Learning1m 51sLearn how to set up Keras, implement a deep learning algorithm, and build data pipelines using KNIME. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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