Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models.
Create Machine Learning Models in Microsoft Azure
Create Machine Learning Models in Microsoft Azure
This course is part of Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate
Instructor: Microsoft
29,397 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.5
(265 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 12 hours
Learn at your own pace
92%
Most learners liked this course
What you'll learn
How to plan and create a working environment for data science workloads on Azure
How to run data experiments and train predictive models
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
8 quizzes, 15 assignments
Taught in English
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This course is part of the Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Microsoft
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There are 3 modules in this course
Data exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data. n this module, you will learn how to use Python to explore, visualize, and manipulate data.You will also learn how regression can be used to create a machine learning model that predicts numeric values. You will use the scikit-learn framework in Python to train and evaluate a regression model.
What's included
7 videos14 readings3 quizzes6 assignments1 discussion prompt
Classification is a kind of machine learning used to categorize items into classes. In this module, you will learn how classification can be used to create a machine learning model that predicts categories, or classes. You will use the scikit-learn framework in Python to train and evaluate a classification model. You will also learn how clustering can be used to create unsupervised machine learning models that group data observations into clusters. You will use the scikit-learn framework in Python to train a clustering model.
What's included
7 videos7 readings2 quizzes6 assignments
In this module, you will learn about the fundamental principles of deep learning, and how to create deep neural network models using PyTorch or Tensorflow. You will also explore the use of convolutional neural networks to create image classification models.
What's included
8 videos4 readings3 quizzes3 assignments1 discussion prompt
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