Scalable Machine Learning with the Microsoft Machine Learning Server
This course provides an introduction to using Microsoft Machine Learning Server to create and scale machine learning experiments. Through detailed code examples in both R and Python, learners will gain an understanding of how to work with Apache Spark and SQL Server. ▼
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Course Feature
Cost:
Free Trial
Provider:
Pluralsight
Certificate:
Paid Certification
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from Pluralsight platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course provides an overview of Scalable Machine Learning with the Microsoft Machine Learning Server. Participants will learn how to import, process, transform, and visualize big data, write custom, scalable, distributed functions which can be executed in a number of compute contexts, use the state of the art machine learning algorithms included in the MicrosoftML package, integrate machine learning experiments into SQL Server, and use the machine learning server with Hadoop and Spark, including integration with popular frameworks such as PySpark, SparkR and Sparklyr. Participants will also spin up an HDInsight cluster in Microsoft Azure, and build a Spark development environment from scracth.
[Applications]
The application of this course can be seen in a variety of ways. For example, it can be used to develop custom machine learning models that can be deployed on the Microsoft Machine Learning Server. It can also be used to integrate machine learning experiments into SQL Server, as well as to use the machine learning server with Hadoop and Spark. Additionally, it can be used to build a Spark development environment from scratch and to spin up an HDInsight cluster in Microsoft Azure.
[Career Paths]
Career Paths:
1. Data Scientist: Data Scientists use their knowledge of mathematics, statistics, and computer science to analyze large datasets and uncover patterns and trends. They use this information to develop predictive models and algorithms that can be used to make decisions and solve problems. Data Scientists are in high demand and the field is expected to continue to grow as more organizations rely on data-driven decision making.
2. Machine Learning Engineer: Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their knowledge of mathematics, statistics, and computer science to develop algorithms and models that can be used to solve complex problems. They also need to be able to work with large datasets and have experience with distributed computing frameworks such as Hadoop and Spark.
3. Big Data Engineer: Big Data Engineers are responsible for designing, developing, and deploying big data solutions. They use their knowledge of mathematics, statistics, and computer science to develop algorithms and models that can be used to solve complex problems. They also need to be able to work with large datasets and have experience with distributed computing frameworks such as Hadoop and Spark.
4. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for designing, developing, and deploying AI solutions. They use their knowledge of mathematics, statistics, and computer science to develop algorithms and models that can be used to solve complex problems. They also need to be able to work with large datasets and have experience with distributed computing frameworks such as Hadoop and Spark.
Developing Trends:
1. Automation: Automation is becoming increasingly important in the field of machine learning and data science. Automation can help reduce the time and effort required to develop and deploy machine learning models.
2. Cloud Computing: Cloud computing is becoming increasingly important in the field of machine learning and data science. Cloud computing can help reduce the cost and complexity of deploying machine learning models.
3. Deep Learning: Deep learning is becoming increasingly important in the field of machine learning and data science. Deep learning can help improve the accuracy and performance of machine learning models.
4. Natural Language Processing: Natural language processing is becoming increasingly important in the field of machine learning and data science. Natural language processing can help improve the accuracy and performance of machine learning models.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree program focuses on the fundamentals of computer science, including programming, software engineering, computer architecture, and algorithms. It also covers topics such as artificial intelligence, machine learning, and data science. This degree is becoming increasingly popular as the demand for data scientists and machine learning engineers grows.
2. Master of Science in Artificial Intelligence: This degree program focuses on the development of artificial intelligence systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and data mining. This degree is becoming increasingly popular as the demand for AI engineers grows.
3. Master of Science in Data Science: This degree program focuses on the fundamentals of data science, including data analysis, data mining, machine learning, and data visualization. It also covers topics such as big data, predictive analytics, and data engineering. This degree is becoming increasingly popular as the demand for data scientists grows.
4. Master of Science in Machine Learning: This degree program focuses on the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. It also covers topics such as natural language processing, computer vision, and robotics. This degree is becoming increasingly popular as the demand for machine learning engineers grows.
Course Provider
Provider Pluralsight's Stats at AZClass
Pluralsight ranked 16th on the Best Medium Workplaces List.
Pluralsight ranked 20th on the Forbes Cloud 100 list of the top 100 private cloud companies in the world.
Pluralsight Ranked on the Best Workplaces for Women List for the second consecutive year.
AZ Class hope that this free trial Pluralsight course can help your Machine Learning skills no matter in career or in further education. Even if you are only slightly interested, you can take Scalable Machine Learning with the Microsoft Machine Learning Server course with confidence!
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