Create Machine Learning Models in Microsoft Azure from Coursera

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.

This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure. You will learn how to run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production.

From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy -to-digest conceptual content and interactive Jupyter notebooks.

If you already have some idea what machine learning is about or you have a strong mathematical background this course is perfect for you. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks. It's also a good place to start if you plan to move beyond classic machine learning and get an education in deep learning and neural networks, which we only introduce here.

This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at cloud scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure . Each course teaches you the concepts and skills that are measured by the exam.

What's inside

Syllabus

Explore data and create models to predict numeric values

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.

Train and evaluate classification and clustering models

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.

Train and evaluate deep learning models

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches essential concepts and tools for building machine learning models

Provides a strong foundation for planning and creating a suitable working environment for data science workloads on Azure

Covers a range of topics from data exploration to deep learning models, providing a comprehensive overview of machine learning

Guided by clear conceptual content and interactive Jupyter notebooks, making it accessible to learners with varying backgrounds

Prepares learners for the Exam DP-100: Designing and Implementing a Data Science Solution on Azure, a valuable certification in the industry

Assumes some prior knowledge of machine learning or a strong mathematical background, which may not be suitable for complete beginners

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 Create Machine Learning Models in Microsoft Azure with these activities:

Introduction to Machine Learning with Python

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This book will provide a comprehensive overview of machine learning concepts and techniques, including regression modeling.

View Introduction to Machine Learning with Python: A... on Amazon

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Read the book and take notes
Complete the practice exercises and assignments
Discuss the concepts with a study group or online forum

Review of Linear Algebra and Statistics

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This review will help strengthen your foundational knowledge of linear algebra and statistics, which are essential for understanding machine learning.

Browse courses on Linear Algebra

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Go over your notes or textbook for linear algebra
Review the concepts of probability, distributions, and hypothesis testing
Solve practice problems to reinforce your understanding

TensorFlow Regression Tutorial

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This tutorial will be helpful to practice the concepts of regression in machine learning using the TensorFlow framework.

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Review the basics of regression and machine learning
Follow the TensorFlow documentation for the regression tutorial
Build a simple regression model using TensorFlow
Evaluate the performance of your model

Five other activities

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Regression Modeling Practice Exercises

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These practice exercises will provide hands-on experience in building and evaluating regression models.

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Solve practice problems on regression modeling
Use a regression library to implement the models
Evaluate the performance of your models
Debug and improve your models

Blog Post on Regression Modeling

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Writing a blog post will help you synthesize your understanding of regression modeling and share your knowledge with others.

Browse courses on Regression

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Choose a topic related to regression modeling
Research the topic and gather information
Write a blog post that explains the topic clearly and concisely
Publish your blog post and share it with others

Machine Learning Project

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This project will allow you to apply the concepts of machine learning and regression to solve a real-world problem.

Browse courses on Machine Learning

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Identify a problem that can be solved using regression
Collect and prepare data for your project
Build and train a regression model
Evaluate the performance of your model
Deploy your model and monitor its performance

Mentor Junior Data Scientists

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Mentoring others will help you solidify your understanding of machine learning and regression while also giving back to the community.

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Volunteer to mentor junior data scientists or students
Provide guidance on machine learning concepts and regression modeling
Review their work and provide feedback
Share your experiences and insights

Contribute to Open-Source Machine Learning Projects

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Contributing to open-source projects will allow you to work on real-world machine learning problems and collaborate with other developers.

Browse courses on Machine Learning

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Find an open-source machine learning project that interests you
Read the project documentation and code
Identify an area where you can contribute
Submit a pull request with your contributions

Career center

Learners who complete Create Machine Learning Models in Microsoft Azure will develop knowledge and skills that may be useful to these careers:

Data Scientist

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. This course can help Data Scientists who are interested in improving their skills in data exploration and analysis, model training and evaluation, and machine learning solution monitoring.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks. It's also a good place to start if you plan to move beyond classic machine learning and get an education in deep learning and neural networks, which we only introduce here. This course can help Machine Learning Engineers who want to gain a better understanding of the principles of machine learning and how to apply them to real-world problems.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

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. Explore the use of convolutional neural networks to create image classification models. These skills may be useful for Software Engineers who want to specialize in machine learning or data science.

See salaries and explore the career path for Software Engineer

Data Engineer

This course can help Data Engineers who are interested in learning more about machine learning and how to apply it to real-world problems. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Data Engineer

Statistician

This course can help Statisticians who are interested in learning more about machine learning and how to apply it to their work. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Statistician

Marketing Analyst

This course may be useful for Marketing Analysts who want to gain a better understanding of the principles of machine learning and how they can be applied to marketing problems. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Marketing Analyst

Product Manager

This course may be useful for Product Managers who want to gain a better understanding of the principles of machine learning and how they can be applied to product development. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Product Manager

Consultant

This course may be useful for Consultants who want to gain a better understanding of the principles of machine learning and how they can be applied to consulting projects. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

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Teacher

This course may be useful for Teachers who want to gain a better understanding of the principles of machine learning and how they can be applied to teaching. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

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Project Manager

This course may be useful for Project Managers who want to gain a better understanding of the principles of machine learning and how they can be applied to project management. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Project Manager

Researcher

This course may be useful for Researchers who want to gain a better understanding of the principles of machine learning and how they can be applied to research. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Researcher

Business Analyst

This course may be useful for Business Analysts who want to gain a better understanding of the principles of machine learning and how they can be applied to business problems. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

This course may be useful for Operations Research Analysts who want to gain a better understanding of the principles of machine learning and how they can be applied to operations research problems. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

Learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course may help build a foundation for a Data Analyst role, as it covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Data Analyst

Financial Analyst

This course may be useful for Financial Analysts who want to gain a better understanding of the principles of machine learning and how they can be applied to financial analysis. The course covers topics such as data exploration and analysis, regression, classification, clustering, and deep learning.

See salaries and explore the career path for Financial Analyst

Create Machine Learning Models in Microsoft Azure

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