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Snehan Kekre
This is a hands-on, guided introduction to using H2O Flow for machine learning. By the end of this project, you will be able to train and evaluate machine learning models with H2O Flow and AutoML, without writing a single line of code! You will use the point and click, web-based interface to H2O called Flow to solve a business analytics problem with machine learning.
Read more
This is a hands-on, guided introduction to using H2O Flow for machine learning. By the end of this project, you will be able to train and evaluate machine learning models with H2O Flow and AutoML, without writing a single line of code! You will use the point and click, web-based interface to H2O called Flow to solve a business analytics problem with machine learning.
Read more
This is a hands-on, guided introduction to using H2O Flow for machine learning. By the end of this project, you will be able to train and evaluate machine learning models with H2O Flow and AutoML, without writing a single line of code! You will use the point and click, web-based interface to H2O called Flow to solve a business analytics problem with machine learning.
H2O is a leading open-source machine learning and artificial intelligence platform trusted by data scientists and machine learning practitioners. It has APIs available in R, Python, Scala, and also a web-based point and click interface called Flow. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipelines such as data pre-processing, feature engineering, and model deployment.
To get the most out of this project, we recommend that you have an understanding of basic machine learning theory, and have trained machine learning models.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/hgkloy/machine
What's inside
Syllabus
Machine Learning with H2O Flow
Welcome to this is a hands-on, guided introduction to using H2O Flow for machine learning. By the end of this project, you will be able to train and evaluate machine learning models with H2O Flow and AutoML, without writing a single line of code! You will use the point and click, web-based interface to H2O called Flow to solve a business analytics problem with machine learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops machine learning theory and modeling skills with H2O Flow, a popular industry platform
Provides hands-on, guided learning that emphasizes intuitive interface and ease of use
Focuses on model building and evaluation without the need for coding, accessible to beginners
Emphasizes the practical application of machine learning, preparing learners for job roles that require it
Save this course
Save Machine Learning with H2O Flow to your list so you can find it easily later:
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Reviews summary
H2o flow machine learning
Learners say that the Machine Learning with H2O Flow course offers practical knowledge and explains topics clearly. The course is well-received with most students giving positive feedback. It includes engaging assignments and teaches students to use H2O Flow for machine learning without coding.
The majority of students gave positive feedback about the course.
"nice"
"Good"
"good"
"Great"
"good "
"great"
"THANKS"
"nice one"
"Thankyou..."
"very useful course"
" TRY THIS ML in H20"
"GREAT TO LEARN !THANK YOU"
"Really informative and great"
"simple and clear , this practical kind of courses are very welcomed"
Topics are explained in a clear and easy-to-understand way.
"Nice Course. The topic is nicely explained."
"Good Explanation and Foundation for me to work in my H2O Project"
Teaches students how to use H2O Flow for machine learning.
"THIS PROJECT TELLS US ABOUT H20 FLOWWE CAN USE IT TO DO ML WITHOUT ANY CODING."
"simple and clear , this practical kind of courses are very welcomed"
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 Machine Learning with H2O Flow with these
activities:
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems'
Show steps
Provides a deeper understanding of machine learning concepts and techniques, reinforcing the content covered in the course.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the book to grasp the fundamentals of machine learning.
-
Work through the practice exercises in the book to reinforce your understanding.
Form a study group to discuss course concepts and work on projects collaboratively
Show steps
Fosters collaboration, enhances understanding through peer-to-peer learning, and provides opportunities for collective problem-solving.
Browse courses on
Machine Learning
Show steps
-
Find fellow students interested in forming a study group.
-
Set regular meeting times to discuss course material and work on projects together.
-
Take turns leading discussions and presenting findings to the group.
Practice coding machine learning algorithms with Kaggle competitions
Show steps
Enhances coding proficiency and deepens understanding of machine learning algorithms through hands-on practice.
Browse courses on
Machine Learning
Show steps
-
Select a Kaggle competition relevant to the course content.
-
Implement machine learning algorithms to solve the competition problem.
-
Evaluate your results and compare them with other participants.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore additional H2O Flow tutorials to enhance your skills
Show steps
Provides opportunities for further exploration and skill development beyond the scope of the course.
Browse courses on
Machine Learning
Show steps
-
Identify areas where you want to enhance your H2O Flow skills.
-
Search for and review relevant H2O Flow tutorials.
-
Follow the tutorials and apply the techniques to your own projects.
Create a blog post summarizing your learnings from the course
Show steps
Encourages reflection and synthesis of the course content, solidifying understanding and promoting retention.
Browse courses on
Machine Learning
Show steps
-
Identify key concepts and techniques covered in the course.
-
Write a blog post summarizing and explaining these concepts and techniques.
-
Share your blog post on social media or a personal website.
Create a collection of resources on machine learning with H2O Flow
Show steps
Encourages organization and synthesis, fostering a deeper understanding of the course content and its applications.
Browse courses on
Machine Learning
Show steps
-
Gather and curate resources on H2O Flow, including tutorials, articles, and code examples.
-
Organize the resources into a logical structure for easy access and navigation.
-
Share the collection with others in your field or online communities.
Attend a workshop on advanced machine learning techniques
Show steps
Exposes students to new and advanced machine learning techniques, fostering continuous learning and professional development.
Browse courses on
Machine Learning
Show steps
-
Identify and register for a workshop that aligns with your interests.
-
Attend the workshop and actively participate in the sessions.
-
Apply the knowledge gained from the workshop to your own projects and research.
Contribute to an open-source machine learning project
Show steps
Provides real-world experience in machine learning development and collaboration, promoting professional growth.
Browse courses on
Machine Learning
Show steps
-
Identify an open-source machine learning project that interests you.
-
Review the project's documentation and contribute bug reports or feature requests.
-
Make code contributions to the project, following the project's guidelines.
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems'
Show steps
Provides a deeper understanding of machine learning concepts and techniques, reinforcing the content covered in the course.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book to grasp the fundamentals of machine learning.
- Work through the practice exercises in the book to reinforce your understanding.
Form a study group to discuss course concepts and work on projects collaboratively
Show steps
Fosters collaboration, enhances understanding through peer-to-peer learning, and provides opportunities for collective problem-solving.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find fellow students interested in forming a study group.
- Set regular meeting times to discuss course material and work on projects together.
- Take turns leading discussions and presenting findings to the group.
Practice coding machine learning algorithms with Kaggle competitions
Show steps
Enhances coding proficiency and deepens understanding of machine learning algorithms through hands-on practice.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Select a Kaggle competition relevant to the course content.
- Implement machine learning algorithms to solve the competition problem.
- Evaluate your results and compare them with other participants.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore additional H2O Flow tutorials to enhance your skills
Show steps
Provides opportunities for further exploration and skill development beyond the scope of the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify areas where you want to enhance your H2O Flow skills.
- Search for and review relevant H2O Flow tutorials.
- Follow the tutorials and apply the techniques to your own projects.
Create a blog post summarizing your learnings from the course
Show steps
Encourages reflection and synthesis of the course content, solidifying understanding and promoting retention.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify key concepts and techniques covered in the course.
- Write a blog post summarizing and explaining these concepts and techniques.
- Share your blog post on social media or a personal website.
Create a collection of resources on machine learning with H2O Flow
Show steps
Encourages organization and synthesis, fostering a deeper understanding of the course content and its applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and curate resources on H2O Flow, including tutorials, articles, and code examples.
- Organize the resources into a logical structure for easy access and navigation.
- Share the collection with others in your field or online communities.
Attend a workshop on advanced machine learning techniques
Show steps
Exposes students to new and advanced machine learning techniques, fostering continuous learning and professional development.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for a workshop that aligns with your interests.
- Attend the workshop and actively participate in the sessions.
- Apply the knowledge gained from the workshop to your own projects and research.
Contribute to an open-source machine learning project
Show steps
Provides real-world experience in machine learning development and collaboration, promoting professional growth.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify an open-source machine learning project that interests you.
- Review the project's documentation and contribute bug reports or feature requests.
- Make code contributions to the project, following the project's guidelines.
Career center
Learners who complete Machine Learning with H2O Flow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Architect
Machine Learning Architect
Machine Learning Architects are responsible for designing and implementing machine learning systems. They work closely with business stakeholders and data scientists to develop and deploy machine learning models. Machine Learning with H2O Flow can be a useful tool for Machine Learning Architects, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Machine Learning Architects to focus on other aspects of their work, such as system design and implementation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work closely with Data Scientists to ensure that models are accurate and efficient. Machine Learning with H2O Flow can be a valuable tool for Machine Learning Engineers, as it can help them to quickly and easily train and evaluate machine learning models. This can save time and allow Machine Learning Engineers to focus on other aspects of their work, such as model optimization and deployment.
Data Scientist
Data Scientist
Data Scientists are experts who develop innovative solutions to complex business problems. They are responsible for collecting, analyzing, and interpreting data in order to identify patterns and trends. Machine Learning with H2O Flow can be a useful tool for Data Scientists, as it can help them to automate the process of training and tuning machine learning models. This can free up Data Scientists to focus on other aspects of their work, such as data pre-processing, feature engineering, and model deployment.
Data Science Manager
Data Science Manager
Data Science Managers are responsible for leading and managing data science teams. They work closely with business stakeholders to identify and solve problems. Machine Learning with H2O Flow can be a useful tool for Data Science Managers, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Data Science Managers to focus on other aspects of their work, such as team management and project planning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and implementing mathematical and statistical models to solve business problems. They work closely with business stakeholders to identify and solve problems. Machine Learning with H2O Flow can be a useful tool for Operations Research Analysts, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Operations Research Analysts to focus on other aspects of their work, such as model development and optimization.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They work closely with business stakeholders to identify and solve problems. Machine Learning with H2O Flow can be a useful tool for Data Analysts, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Data Analysts to focus on other aspects of their work, such as data visualization and communication.
Data Engineer
Data Engineer
Data Engineers are responsible for designing and building data pipelines. They work closely with Data Scientists and Machine Learning Engineers to ensure that data is available and in the right format for analysis and modeling. Machine Learning with H2O Flow can be a useful tool for Data Engineers, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Data Engineers to focus on other aspects of their work, such as data integration and optimization.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and implementing mathematical and statistical models. They work closely with portfolio managers and traders to make investment decisions. Machine Learning with H2O Flow can be a useful tool for Quantitative Analysts, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Quantitative Analysts to focus on other aspects of their work, such as model development and optimization.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They work in a variety of fields, including healthcare, finance, and marketing. Machine Learning with H2O Flow can be a useful tool for Statisticians, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Statisticians to focus on other aspects of their work, such as data analysis and interpretation.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting and analyzing data about markets and consumers. They work closely with businesses to help them make informed decisions about product development and marketing strategies. Machine Learning with H2O Flow can be a useful tool for Market Researchers, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Market Researchers to focus on other aspects of their work, such as data analysis and interpretation.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business problems and developing solutions. They work closely with stakeholders to gather requirements and define solutions. Machine Learning with H2O Flow can be a useful tool for Business Analysts, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Business Analysts to focus on other aspects of their work, such as developing and implementing solutions.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work closely with stakeholders to gather requirements and define solutions. Machine Learning with H2O Flow can be a useful tool for Software Engineers, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Software Engineers to focus on other aspects of their work, such as software development and testing.
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risks. They work closely with insurance companies and other financial institutions to develop and implement risk management strategies. Machine Learning with H2O Flow can be a useful tool for Actuaries, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Actuaries to focus on other aspects of their work, such as risk assessment and mitigation.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. They work closely with clients to help them make informed investment decisions. Machine Learning with H2O Flow can be a useful tool for Financial Analysts, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Financial Analysts to focus on other aspects of their work, such as investment analysis and portfolio management.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks. They work closely with business stakeholders to develop and implement risk management strategies. Machine Learning with H2O Flow can be a useful tool for Risk Analysts, as it can help them to quickly and easily explore data and identify patterns. This can save time and allow Risk Analysts to focus on other aspects of their work, such as risk assessment and mitigation.
For more career information including salaries, visit:
OpenCourser.com/course/hgkloy/machine
Reading list
We've selected six 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
Machine Learning with H2O Flow.
Provides a comprehensive overview of deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about deep learning with Python.
Save
Provides a comprehensive overview of deep learning with R, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about deep learning with R.
Provides a comprehensive overview of machine learning for computer vision, covering topics such as image classification, object detection, and image segmentation. It valuable resource for anyone who wants to learn more about machine learning for computer vision.
Provides a comprehensive overview of machine learning with Python, covering topics such as data preparation, model training, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning with Python.
Provides a comprehensive overview of machine learning with R, covering topics such as data preparation, model training, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning with R.
Provides a comprehensive overview of machine learning with Python, covering topics such as data preparation, model training, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hgkloy/machine
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Effort
1.2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Develops machine learning theory and modeling skills with H2O Flow, a popular industry platform
Provides hands-on, guided learning that emphasizes intuitive interface and ease of use
Focuses on model building and evaluation without the need for coding, accessible to beginners
Emphasizes the practical application of machine learning, preparing learners for job roles that require it
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