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Laura Ramov
Did you know that you can use Azure Machine Learning to help you analyze data?
In this 1-hour project-based course, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data. We will calculate its descriptive statistics and correlations, train a machine learning model and calculate its feature importance to see how features affect the label and visualize categorical data, as well as relationships between variables, in Jupyter notebook.
Read more
Did you know that you can use Azure Machine Learning to help you analyze data?
In this 1-hour project-based course, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data. We will calculate its descriptive statistics and correlations, train a machine learning model and calculate its feature importance to see how features affect the label and visualize categorical data, as well as relationships between variables, in Jupyter notebook.
Read more
Did you know that you can use Azure Machine Learning to help you analyze data?
In this 1-hour project-based course, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data. We will calculate its descriptive statistics and correlations, train a machine learning model and calculate its feature importance to see how features affect the label and visualize categorical data, as well as relationships between variables, in Jupyter notebook.
In order to be successful in this project, you will need knowledge of Python language and experience with machine learning in Python. Also, Azure subscription is required (free trial is an option for those who don’t have it), as well as Azure Machine Learning resource and a compute instance within. Instructional links will be provided to guide you through creation, if needed, in the first task.
If you are ready to learn how to analyze data, this is a course for you! Let’s get started!
Register for this course and see more details by visiting:
OpenCourser.com/course/b9lay5/analyze
What's inside
Syllabus
Project Overview
In this guided project, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data. We will calculate its descriptive statistics and correlations, train a machine learning model and calculate its feature importance to see how features affect the label and visualize categorical data, as well as relationships between variables in Jupyter notebook.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with a background in Python and machine learning in Python
Requires an Azure subscription
Has a free trial option for those who don't have an Azure subscription
Save this course
Save Analyze Data in Azure ML Studio to your list so you can find it easily later:
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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 Analyze Data in Azure ML Studio with these
activities:
Review Python Fundamentals
Show steps
Strengthen your programming foundation by reviewing Python concepts.
Browse courses on
Python
Show steps
-
Review Python syntax and data structures.
-
Practice writing simple Python programs.
Join a Study Group
Show steps
Enhance your understanding by collaborating with peers in a study group.
Show steps
-
Form a study group with other students in the course.
-
Meet regularly to discuss course material, share notes, and solve problems together.
Azure Machine Learning Tutorial
Show steps
Reinforce the concepts learned about Azure Machine Learning by following an external tutorial.
Browse courses on
Azure Machine Learning
Show steps
-
Identify a tutorial that covers relevant concepts.
-
Follow the tutorial step-by-step.
-
Experiment with the provided code and try to understand the implementation.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice with Azure Machine Learning Exercises
Show steps
Enhance your practical skills in Azure Machine Learning by completing a set of exercises.
Browse courses on
Azure Machine Learning
Show steps
-
Find a set of exercises or problems related to Azure Machine Learning.
-
Attempt to solve the exercises on your own.
-
Refer to documentation or seek assistance if you encounter difficulties.
-
Review your solutions and identify areas for improvement.
Develop a Machine Learning Model using Azure
Show steps
Apply your knowledge of Azure Machine Learning by creating and deploying a machine learning model.
Show steps
-
Define the problem you want to solve with machine learning.
-
Gather and preprocess the necessary data.
-
Train a machine learning model using Azure Machine Learning.
-
Evaluate the performance of the model and make adjustments as needed.
-
Deploy the model to a production environment.
Build a Personal Recommendation System
Show steps
Enhance your understanding of machine learning by building a personal recommendation system using Azure Machine Learning.
Show steps
-
Gather a dataset of items and user preferences.
-
Use Azure Machine Learning to train a collaborative filtering model.
-
Develop a user interface for the recommendation system.
-
Deploy the recommendation system and collect user feedback.
-
Iterate on the system based on feedback and usage data.
Contribute to an Open Source Machine Learning Project
Show steps
Expand your knowledge by contributing to the Azure Machine Learning open source community.
Browse courses on
Open Source
Show steps
-
Identify an open source machine learning project related to Azure Machine Learning.
-
Find a specific area or issue to work on.
-
Submit a pull request with your contribution.
Review Python Fundamentals
Show steps
Strengthen your programming foundation by reviewing Python concepts.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data structures.
- Practice writing simple Python programs.
Join a Study Group
Show steps
Enhance your understanding by collaborating with peers in a study group.
Show steps
Show steps
Show steps
- Form a study group with other students in the course.
- Meet regularly to discuss course material, share notes, and solve problems together.
Azure Machine Learning Tutorial
Show steps
Reinforce the concepts learned about Azure Machine Learning by following an external tutorial.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Identify a tutorial that covers relevant concepts.
- Follow the tutorial step-by-step.
- Experiment with the provided code and try to understand the implementation.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice with Azure Machine Learning Exercises
Show steps
Enhance your practical skills in Azure Machine Learning by completing a set of exercises.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Find a set of exercises or problems related to Azure Machine Learning.
- Attempt to solve the exercises on your own.
- Refer to documentation or seek assistance if you encounter difficulties.
- Review your solutions and identify areas for improvement.
Develop a Machine Learning Model using Azure
Show steps
Apply your knowledge of Azure Machine Learning by creating and deploying a machine learning model.
Show steps
Show steps
Show steps
- Define the problem you want to solve with machine learning.
- Gather and preprocess the necessary data.
- Train a machine learning model using Azure Machine Learning.
- Evaluate the performance of the model and make adjustments as needed.
- Deploy the model to a production environment.
Build a Personal Recommendation System
Show steps
Enhance your understanding of machine learning by building a personal recommendation system using Azure Machine Learning.
Show steps
Show steps
Show steps
- Gather a dataset of items and user preferences.
- Use Azure Machine Learning to train a collaborative filtering model.
- Develop a user interface for the recommendation system.
- Deploy the recommendation system and collect user feedback.
- Iterate on the system based on feedback and usage data.
Contribute to an Open Source Machine Learning Project
Show steps
Expand your knowledge by contributing to the Azure Machine Learning open source community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open source machine learning project related to Azure Machine Learning.
- Find a specific area or issue to work on.
- Submit a pull request with your contribution.
Career center
Learners who complete Analyze Data in Azure ML Studio will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for the development of machine learning models. These engineers design, implement, and maintain ML models, which are used for predictive analytics, natural language processing, and computer vision, among other tasks. If you wish to design and build ML models using Azure ML Studio, a course on how to analyze data in Azure ML may be useful to your career goals.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods and algorithms to extract knowledge from data. The data used can come from a variety of sources and formats, and the goal is to help businesses make data-driven decisions. If you seek a career in data science and wish to use Azure ML Studio as a tool of your trade, a course on data analysis in Azure ML Studio may be helpful.
Data Analyst
Data Analyst
A Data Analyst analyzes data to identify trends and patterns, which can help businesses make better decisions. Data Analysts transform raw data to be used for reporting, modeling, and decision making. If you wish to enter this field and learn how to analyze data in Azure ML Studio, this course may be a good fit for you.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use their knowledge of statistics to solve problems and make informed decisions. If you wish to use statistics to solve problems and make informed decisions, a course on data analysis in Azure ML Studio may be helpful.
Research Scientist
Research Scientist
Research Scientists conduct scientific research to advance knowledge and understanding in various fields. They may be involved in designing and conducting experiments, analyzing data, and developing new theories and models. If you wish to analyze data as part of your scientific research and learn how to do so in Azure ML Studio, a course on this tool may prove helpful.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. They may develop and use complex models and algorithms to analyze data, identify trends, and make predictions. If you wish to analyze financial data using models and algorithms, a course on how to analyze data in Azure ML Studio may help you gain the skills needed to succeed in this role.
Data Engineer
Data Engineer
A Data Engineer designs and builds systems to process and store data. After data is collected, it must be housed in a way that makes it easy to find, access, and use. Data Engineers create these systems to ensure that data is available and properly formatted for use in analytics.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts apply analytical methods to help organizations make better decisions. They use a variety of techniques to improve efficiency and productivity across all industries. If you wish to apply methods to help improve efficiency and productivity, a course on data analysis in Azure ML Studio may be helpful.
Technical Consultant
Technical Consultant
Technical Consultants provide expert advice and guidance to clients on technology-related issues. They may help clients implement new technologies, optimize existing systems, or troubleshoot problems. If you wish to provide expert advice on how to analyze data using Azure ML Studio, it may be useful to take a course on the subject.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers create systems that help solve medical problems. These engineers may design or help build artificial organs or other medical devices, leading to significant advancements in healthcare and medical technology. If you wish to design systems using machine learning to address medical problems, a course on how to analyze data in Azure ML Studio may prove useful.
Web Developer
Web Developer
Web Developers design and build websites and web applications. They may use their programming skills to analyze data for use in different web applications. If you wish to build web applications and analyze data, a course on data analysis in Azure ML Studio may be useful to you.
Software Developer
Software Developer
Software Developers design, develop, and maintain applications and software. They may use their programming skills to analyze data for use in different applications. If you wish to build software applications and analyze data, a course on data analysis in Azure ML Studio may be useful to you.
Business Analyst
Business Analyst
A Business Analyst identifies the needs of a business and helps determine how IT can fulfill those needs. Business Analysts may use data to gain insight into improving business operations. If you wish to leverage data to help improve business operations, a course on how to analyze data in Azure ML Studio may help.
For more career information including salaries, visit:
OpenCourser.com/course/b9lay5/analyze
Reading list
We've selected 12 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
Analyze Data in Azure ML Studio.
Good companion book to this course. It covers more in-depth topics in machine learning with Python, with a focus on practical applications. It can be used as a reference guide for more advanced topics.
Good resource for learning the basics of Python for data analysis. It covers topics such as data manipulation, data visualization, and machine learning. It can be used as a prerequisite book for this course.
Good resource for learning the fundamentals of data science, including data wrangling, data analysis, and machine learning. It can be used as a prerequisite book for this course.
Classic textbook on statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees. It can be used as a reference guide for more advanced topics.
Save
Classic textbook on data mining. It covers a wide range of topics, including data preprocessing, clustering, and classification. It can be used as a reference guide for more advanced topics.
Hands-on guide to machine learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees. It can be used as a reference guide for more advanced topics.
Good resource for learning machine learning with R. It covers a wide range of topics, including data preprocessing, clustering, and classification. It can be used as a reference guide for more advanced topics.
Good resource for learning deep learning with Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It can be used as a reference guide for more advanced topics.
Good resource for learning natural language processing with Python. It covers a wide range of topics, including text preprocessing, text classification, and text generation. It can be used as a reference guide for more advanced topics.
Good resource for learning computer vision with Python. It covers a wide range of topics, including image preprocessing, object detection, and image segmentation. It can be used as a reference guide for more advanced topics.
Classic textbook on reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradients. It can be used as a reference guide for more advanced topics.
Good resource for learning statistical modeling and computation. It covers a wide range of topics, including linear models, generalized linear models, and Bayesian models. It can be used as a reference guide for more advanced topics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b9lay5/analyze
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Laura Ramov
Language
English
Good to know
Suitable for learners with a background in Python and machine learning in Python
Requires an Azure subscription
Has a free trial option for those who don't have an Azure subscription
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