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
This course is part of Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate
Instructor: Microsoft
Sponsored by FutureX
30,475 already enrolled
(272 reviews)
Recommended experience
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
- PyTorch (Machine Learning Library)
- Cloud Infrastructure
- Applied Machine Learning
- Machine Learning Algorithms
- Statistical Machine Learning
- Predictive Analytics
- Unsupervised Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Microsoft Azure
- Artificial Intelligence
- Predictive Modeling
- Cloud Platforms
- Scikit Learn (Machine Learning Library)
- Machine Learning
- Analytics
- Data Science
- Machine Learning Software
- Supervised Learning
- Cloud Solutions
- Machine Learning Methods
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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. In 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 readings9 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 readings8 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 readings6 assignments1 discussion prompt
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Reviewed on Aug 5, 2023
The journey was really interesting. I'm really inspired to go on this course till its end.
Reviewed on Apr 27, 2023
Good course with a focus on helping the learner to better understand ML concepts and the coding associated to it before diving deep into Azure tool
Reviewed on Feb 7, 2023
This course is very easy to understand and have a great value for new data science professionals. To the point explanations and engaging content by team Microsoft.
Recommended if you're interested in Computer Science
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