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Saravanan Dhandapani

It's common to spend a lot of resources on identifying an optimal machine learning model. In this course, you will learn how to simplify this process using various cutting edge features offered by Microsoft Azure Machine Learning service.

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It's common to spend a lot of resources on identifying an optimal machine learning model. In this course, you will learn how to simplify this process using various cutting edge features offered by Microsoft Azure Machine Learning service.

Many developers struggle with the time and effort it takes in designing and developing a highly optimal machine learning model. In this course, Developing Models in Microsoft Azure, you will learn the foundational knowledge of machine learning solutions offered by Microsoft Azure. First, you will understand the basics of setting up workspaces, creating blobstore, and making and registering datasets. Next, you will explore how to create an experiment and submit the run on a compute target provided by Microsoft Azure. Finally, you will explore how to tune hyperparameters and use automated machine learning models, allowing for the development of models without writing a single line of code. When you are finished with this course, you will have the skills and knowledge on the various cutting edge features offered by Microsoft Azure Machine Learning service that are necessary in developing a machine learning model.

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What's inside

Syllabus

Course Overview
Understanding Azure Machine Learning service
Import and Prepare Data for Modeling
Training, Tracking, and Monitoring a Model
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Tuning Hyperparameters and AutoML

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Imparts foundational knowledge of machine learning solutions offered by Microsoft Azure
Develops essential skills in setting up workspaces, creating blobstores, and making and registering datasets
Enhances understanding of designing and developing highly optimal machine learning models
Provides practical experience in creating experiments and submitting runs on Microsoft Azure
Empowers learners to leverage cutting-edge features of the Microsoft Azure Machine Learning service
Suitable for developers seeking deeper knowledge in designing and deploying machine learning models

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Developing Models in Microsoft Azure with these activities:
Probability and Statistics Review
Brush up on your probability and statistics knowledge to ensure a strong foundation for machine learning.
Browse courses on Probability
Show steps
  • Review your notes from a previous probability and statistics course.
  • Take practice problems.
  • Complete online tutorials or exercises.
  • Seek help from a tutor or mentor if needed.
Study Group
Connect with other students to discuss course material and work on problems together.
Browse courses on Machine Learning
Show steps
  • Find a study buddy or group.
  • Schedule regular study sessions.
  • Discuss course material, work on problems, and share resources.
  • Help each other learn and stay motivated.
Machine Learning with Python: Hands-On for Developers and Data Scientists
Familiarize yourself with the fundamental concepts of machine learning and Python programming.
Show steps
  • Read the first five chapters of the book.
  • Complete the hands-on exercises in the book.
  • Build a small machine learning project using Python.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Coursera Course: Machine Learning
Enroll in a structured course to deepen your understanding of machine learning and its applications.
Browse courses on Machine Learning
Show steps
  • Enroll in the Coursera course 'Machine Learning'.
  • Watch the video lectures and complete the assignments.
  • Participate in the discussion forums.
  • Complete the final project.
Kaggle Competitions
Challenge yourself to predict outcomes and gain experience working on real-world datasets.
Browse courses on Machine Learning
Show steps
  • Choose a Kaggle competition that aligns with your interests.
  • Download the competition dataset and explore it.
  • Build and submit a model to the competition.
  • Analyze your results and iterate on your model.
Machine Learning Blog Post
Summarize and share your learnings on a particular machine learning topic.
Browse courses on Machine Learning
Show steps
  • Choose a specific machine learning topic to write about.
  • Research the topic thoroughly and gather relevant information.
  • Write a blog post that explains the topic clearly and concisely.
  • Publish your blog post and share it with others.
Machine Learning Project
Apply your machine learning skills to solve a real-world problem.
Browse courses on Machine Learning
Show steps
  • Identify a problem that can be solved using machine learning.
  • Gather and prepare data for your project.
  • Build and train a machine learning model.
  • Evaluate your model's performance.
  • Deploy your model and track its performance.

Career center

Learners who complete Developing Models in Microsoft Azure will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers use their knowledge in mathematics, computer science, and machine learning algorithms to develop and maintain machine learning systems. This course will provide you with the skills necessary for entry-level positions.
Data Analyst
Data Analysts can benefit from taking this course to learn the foundational knowledge of machine learning solutions offered by Microsoft Azure.
Data Scientist
Data Scientists are responsible for developing machine learning models. This course may be useful for someone interested in getting into this field as it will help build a foundation in machine learning model development.
Consultant
Consultants will find this course useful as it will teach you how to simplify the process of identifying an optimal machine learning model using various cutting edge features offered by Microsoft Azure Machine Learning service.
Software Engineer
Software Engineers will find this course helpful as it will teach you how to simplify the process of identifying an optimal machine learning model using various cutting edge features offered by Microsoft Azure Machine Learning service.
Data Engineer
Data Engineers may find this course useful for learning how to use Azure Machine Learning service which can be added to their skillset.
Business Analyst
This course will teach Business Analysts how to use various cutting edge features offered by Microsoft Azure Machine Learning, making you more competitive in the modern job market.
Operations Research Analyst
Operations Research Analysts will find this course helpful for learning how to use Azure Machine Learning service.
Statistician
This course will help Statisticians learn how to use Azure Machine Learning service. This is a valuable skill in today's job market.
Financial Analyst
This course can help Financial Analysts learn about Azure Machine Learning service, which is a valuable skill in today's job market.
Project Manager
This course can be helpful for Project Managers who are working on machine learning projects, as it will provide you with a foundational knowledge of machine learning solutions offered by Microsoft Azure.
Product Manager
Product Managers may find this course useful for learning about the foundational knowledge of machine learning solutions offered by Microsoft Azure.
Marketing Manager
Marketing Managers who are working with clients in the technology industry may find this course useful for learning about the foundational knowledge of machine learning solutions offered by Microsoft Azure.
Sales Manager
Sales Managers who are working with clients in the technology industry may find this course useful for learning about the foundational knowledge of machine learning solutions offered by Microsoft Azure.
Human Resources Manager
Human Resources Managers will likely not be directly involved in the tasks taught in this course, however, it may be useful for learning about the foundational knowledge of machine learning solutions offered by Microsoft Azure.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Developing Models in Microsoft Azure.
Good resource for learners who want to learn about the practical aspects of machine learning. It provides a step-by-step guide to building and deploying machine learning models, with a focus on how to use open source tools.
Good resource for learners who want to get a quick overview of the concepts and techniques of machine learning. It provides a concise and easy-to-understand explanation of the different concepts, with a focus on how to apply them in practice.
Good resource for learners who want to learn how to use Scikit-Learn, Keras, and TensorFlow to build and deploy machine learning models. It provides a step-by-step guide to the different steps involved in the machine learning process, with a focus on how to use these tools effectively.
Good resource for learners who are new to machine learning and Python. It provides a gentle introduction to the concepts and techniques of machine learning, with a focus on how to use Python to build and deploy machine learning models.
Good resource for learners who want to learn about deep learning with Python. It provides a detailed explanation of the concepts and techniques of deep learning, with a focus on how to use Python to build and deploy deep learning models.
Good resource for learners who want to learn about the Bayesian approach to machine learning. It provides a detailed explanation of the Bayesian approach, along with examples of how to use it in practice.
Good resource for learners who want to learn about probabilistic graphical models. It provides a detailed explanation of the different types of probabilistic graphical models, along with examples of how to use them in practice.

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