DP-100 Part 2 - Modeling faq

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This course is part two of a three part series, focusing on preparation for the DP-100 exam. It covers how to create models using Azure Machine Learning designer, run training scripts in an Azure Machine Learning workspace, generate metrics from an experiment run, and build a foundation using key algorithms, features, and machine learning models. It also covers important tools such as PyTorch, Scikit-learn, Keras, and Chainer.

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A Cloud Guru

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Course Overview

❗The content presented here is sourced directly from A Cloud Guru platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 20th, 2023]

The DP-100 Part 2 - Modeling course focuses on how to run experiments and train models in Azure Machine Learning. This course is part two of a three part series, designed to prepare students for the DP-100 exam. Through this course, students will learn how to create models using Azure Machine Learning designer, run training scripts in an Azure Machine Learning workspace, generate metrics from an experiment run, and build a foundation using key algorithms, features, and machine learning models. Additionally, students will gain experience with important tools such as PyTorch, Scikit-learn, Keras, and Chainer.

[Applications]
After completing this course, students should be able to apply the concepts learned to create and train models in Azure Machine Learning. They should be able to use the key algorithms, features, and machine learning models to build a foundation for their own projects. Additionally, they should be able to use the important tools such as PyTorch, Scikit-learn, Keras, and Chainer to create and train models.

[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of mathematics, statistics, and computer science to analyze large datasets and uncover insights. They use a variety of tools and techniques to develop predictive models and uncover patterns in data. Data Scientists are in high demand and the field is expected to continue to grow in the coming years.

2. Machine Learning Engineer: Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use a variety of tools and techniques to build and optimize models, and they must be able to interpret and explain the results of their models. This is a rapidly growing field and is expected to continue to grow in the coming years.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for designing, developing, and deploying AI-based systems. They use a variety of tools and techniques to build and optimize AI-based systems, and they must be able to interpret and explain the results of their systems. This is a rapidly growing field and is expected to continue to grow in the coming years.

4. Deep Learning Engineer: Deep Learning Engineers are responsible for designing, developing, and deploying deep learning models. They use a variety of tools and techniques to build and optimize deep learning models, and they must be able to interpret and explain the results of their models. This is a rapidly growing field and is expected to continue to grow in the coming years.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and data science. This degree path is ideal for those looking to develop a strong foundation in computer science and its related fields.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence and machine learning algorithms. It covers topics such as natural language processing, computer vision, robotics, and deep learning. This degree path is ideal for those looking to specialize in the development of AI and ML algorithms.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of data. It covers topics such as data mining, data visualization, and predictive analytics. This degree path is ideal for those looking to specialize in the analysis and interpretation of data.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms. It covers topics such as deep learning, reinforcement learning, and probabilistic models. This degree path is ideal for those looking to specialize in the development of advanced ML algorithms.

The development of AI and ML technologies is rapidly evolving, and these degree paths are becoming increasingly popular. As the demand for AI and ML professionals continues to grow, these degree paths will become even more important for those looking to specialize in these fields.

Course Provider

Provider A Cloud Guru's Stats at AZClass

DP-100 Part 2 - Modeling is the second part of a three-part series focusing on preparation for the DP-100 exam. It covers how to create models using the Azure Machine Learning designer, run training scripts in an Azure Machine Learning workspace, generate metrics from experiment runs, and build a foundation with key algorithms, functions, and machine learning models. It also covers important tools like PyTorch, Scikit-learn, Keras, and Chainer. This course is the second in a three-part series focusing on preparation for the DP-100 exam.

Rating Grade: B This is a trending provider perfect for gaining traction and maybe a good option for users who are looking for a reliable source of learning content.

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