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Automatic Machine Learning with H2O AutoML and Python
Snehan Kekre
Snehan Kekre
This is a hands-on, guided project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. 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 pipeline such as data pre-processing, feature engineering and model deployment.
Read more
This is a hands-on, guided project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. 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 pipeline such as data pre-processing, feature engineering and model deployment.
Read more
This is a hands-on, guided project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. 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 pipeline such as data pre-processing, feature engineering and model deployment.
To successfully complete the project, we recommend that you have prior experience in Python programming, basic machine learning theory, and have trained ML models with a library such as scikit-learn. We will not be exploring how any particular model works nor dive into the math behind them. Instead, we assume you have this foundational knowledge and want to learn to use H2O's AutoML interface for automatic machine learning.
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/e3o5im/automatic
What's inside
Syllabus
Automatic Machine Learning with H2O AutoML and Python
Welcome to this hands-on project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. 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 pipeline such as data pre-processing, feature engineering and model deployment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Ideal for learners with a Python, machine learning, and scikit-learn foundation
Focuses on data science and machine learning pipeline aspects beyond creating ML models
Best suited for learners based in the North America region
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Save Automatic Machine Learning with H2O AutoML and Python to your list so you can find it easily later:
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Reviews summary
Valuable automl learning experience
Learners overwhelmingly praise this automatic machine learning course. They appreciate the clear and well-structured project format, finding it useful and effective for learning. The practical nature of the course is also a positive for students looking to apply what they learn. Overall, learners highly recommend this course for gaining a strong foundation in H2O AutoML and Python.
Excellent entry-level course
"it was good."
"Very Recomended for knowledge foundation about H2O ML"
Well presented lessons and project explainers
"Every step in this project was well explained and there was a nice flow."
"Introductory stuff. But concise and useful as an introduction."
Useful real world applciations
"You can aply what you learn to many projects"
"An awesome short guide to work on H2O AutoML"
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 Automatic Machine Learning with H2O AutoML and Python with these
activities:
Refresh skill: Review basic Python syntax
Show steps
Prepare for the course by reviewing basic Python syntax, as it is essential for understanding H2O AutoML and its Python API.
Browse courses on
Python
Show steps
-
Review online tutorials or documentation on Python fundamentals (variables, data types, control flow).
-
Complete beginner-level Python coding exercises or challenges.
Seek guidance from experienced machine learning practitioners
Show steps
Connect with experts in the field to receive personalized advice and support.
Show steps
-
Identify potential mentors through networking events or online platforms
-
Reach out to mentors and request guidance on AutoML
-
Schedule regular meetings or calls to discuss progress and challenges
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Expand knowledge of machine learning concepts and techniques used in conjunction with AutoML.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read chapters relevant to supervised learning models and model evaluation
-
Work through code examples and exercises
-
Apply the concepts to practical problems
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Practice training ML models with scikit-learn
Show steps
Reinforce foundational knowledge of machine learning models and sharpen skills in training and tuning models.
Show steps
-
Review scikit-learn documentation on supervised learning models
-
Train and evaluate models on a simple dataset
-
Experiment with different hyperparameters and feature preprocessing techniques
Follow online tutorials to expand your understanding of machine learning concepts
Show steps
Deepen your knowledge of essential machine learning concepts and techniques, complementing the course materials.
Browse courses on
Machine Learning
Show steps
-
Identify reputable online platforms or instructors providing tutorials relevant to the course.
-
Select tutorials that cover topics related to H2O AutoML or general machine learning principles.
-
Follow the tutorials, take notes, and experiment with the concepts presented.
Follow tutorials on H2O's AutoML interface
Show steps
Gain hands-on experience with the H2O AutoML interface and understand its features and capabilities.
Show steps
-
Explore H2O's documentation on AutoML
-
Follow step-by-step tutorials to run AutoML experiments
-
Analyze and interpret the results of AutoML runs
Join a study group for the course
Show steps
Enhance your learning experience by collaborating with peers, discussing course concepts, and providing mutual support.
Show steps
-
Reach out to classmates or join online forums to find potential study group members.
-
Establish regular meeting times and designate responsibilities for topic discussions.
-
Prepare for meetings by reviewing course materials and identifying areas for discussion.
Join a study group or online forum for AutoML
Show steps
Collaborate with peers, share knowledge, and receive feedback on your understanding of AutoML.
Show steps
-
Identify a study group or online forum focused on AutoML
-
Participate in discussions, ask questions, and share insights
-
Review and provide feedback on others' work
Start project: Experiment with H2O AutoML on a personal dataset
Show steps
Solidify your understanding of H2O AutoML by applying it to a practical problem with a personal dataset.
Show steps
-
Choose a dataset that aligns with your interests (e.g., healthcare, finance, sports).
-
Load the dataset into an H2O cluster.
-
Use H2O AutoML to train and evaluate models on your dataset.
-
Analyze the results and identify the best model for your problem.
Create a presentation on a business analytics problem
Show steps
Apply knowledge gained in the course to a practical setting, developing critical thinking and communication skills.
Show steps
-
Identify a business analytics problem and gather relevant data
-
Use AutoML to train and evaluate models for the problem
-
Analyze and interpret the results to draw actionable insights
-
Create a presentation to communicate the findings and recommendations
Attend industry meetups or conferences on machine learning
Show steps
Connect with professionals in the field, learn about industry best practices, and stay updated on the latest advancements.
Show steps
-
Research industry events and conferences
-
Attend sessions and workshops related to machine learning and AutoML
-
Network with speakers and attendees
Create a presentation on a specific use case of H2O AutoML
Show steps
Reinforce your comprehension of H2O AutoML and its practical applications by presenting a detailed use case.
Show steps
-
Choose a specific industry or problem domain (e.g., healthcare, finance, fraud detection).
-
Research and gather data to support your use case.
-
Design and implement an H2O AutoML pipeline to address the problem.
-
Develop a presentation to showcase your findings, including model evaluation results and insights.
Develop a machine learning model for a personal project
Show steps
Gain practical experience in applying machine learning to solve real-world problems.
Show steps
-
Identify a personal project that can benefit from machine learning
-
Gather and prepare data for the project
-
Use AutoML to train and evaluate models for the project
-
Deploy the best model and track its performance
Refresh skill: Review basic Python syntax
Show steps
Prepare for the course by reviewing basic Python syntax, as it is essential for understanding H2O AutoML and its Python API.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online tutorials or documentation on Python fundamentals (variables, data types, control flow).
- Complete beginner-level Python coding exercises or challenges.
Seek guidance from experienced machine learning practitioners
Show steps
Connect with experts in the field to receive personalized advice and support.
Show steps
Show steps
Show steps
- Identify potential mentors through networking events or online platforms
- Reach out to mentors and request guidance on AutoML
- Schedule regular meetings or calls to discuss progress and challenges
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Expand knowledge of machine learning concepts and techniques used in conjunction with AutoML.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to supervised learning models and model evaluation
- Work through code examples and exercises
- Apply the concepts to practical problems
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Practice training ML models with scikit-learn
Show steps
Reinforce foundational knowledge of machine learning models and sharpen skills in training and tuning models.
Show steps
Show steps
Show steps
- Review scikit-learn documentation on supervised learning models
- Train and evaluate models on a simple dataset
- Experiment with different hyperparameters and feature preprocessing techniques
Follow online tutorials to expand your understanding of machine learning concepts
Show steps
Deepen your knowledge of essential machine learning concepts and techniques, complementing the course materials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify reputable online platforms or instructors providing tutorials relevant to the course.
- Select tutorials that cover topics related to H2O AutoML or general machine learning principles.
- Follow the tutorials, take notes, and experiment with the concepts presented.
Follow tutorials on H2O's AutoML interface
Show steps
Gain hands-on experience with the H2O AutoML interface and understand its features and capabilities.
Show steps
Show steps
Show steps
- Explore H2O's documentation on AutoML
- Follow step-by-step tutorials to run AutoML experiments
- Analyze and interpret the results of AutoML runs
Join a study group for the course
Show steps
Enhance your learning experience by collaborating with peers, discussing course concepts, and providing mutual support.
Show steps
Show steps
Show steps
- Reach out to classmates or join online forums to find potential study group members.
- Establish regular meeting times and designate responsibilities for topic discussions.
- Prepare for meetings by reviewing course materials and identifying areas for discussion.
Join a study group or online forum for AutoML
Show steps
Collaborate with peers, share knowledge, and receive feedback on your understanding of AutoML.
Show steps
Show steps
Show steps
- Identify a study group or online forum focused on AutoML
- Participate in discussions, ask questions, and share insights
- Review and provide feedback on others' work
Start project: Experiment with H2O AutoML on a personal dataset
Show steps
Solidify your understanding of H2O AutoML by applying it to a practical problem with a personal dataset.
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests (e.g., healthcare, finance, sports).
- Load the dataset into an H2O cluster.
- Use H2O AutoML to train and evaluate models on your dataset.
- Analyze the results and identify the best model for your problem.
Create a presentation on a business analytics problem
Show steps
Apply knowledge gained in the course to a practical setting, developing critical thinking and communication skills.
Show steps
Show steps
Show steps
- Identify a business analytics problem and gather relevant data
- Use AutoML to train and evaluate models for the problem
- Analyze and interpret the results to draw actionable insights
- Create a presentation to communicate the findings and recommendations
Attend industry meetups or conferences on machine learning
Show steps
Connect with professionals in the field, learn about industry best practices, and stay updated on the latest advancements.
Show steps
Show steps
Show steps
- Research industry events and conferences
- Attend sessions and workshops related to machine learning and AutoML
- Network with speakers and attendees
Create a presentation on a specific use case of H2O AutoML
Show steps
Reinforce your comprehension of H2O AutoML and its practical applications by presenting a detailed use case.
Show steps
Show steps
Show steps
- Choose a specific industry or problem domain (e.g., healthcare, finance, fraud detection).
- Research and gather data to support your use case.
- Design and implement an H2O AutoML pipeline to address the problem.
- Develop a presentation to showcase your findings, including model evaluation results and insights.
Develop a machine learning model for a personal project
Show steps
Gain practical experience in applying machine learning to solve real-world problems.
Show steps
Show steps
Show steps
- Identify a personal project that can benefit from machine learning
- Gather and prepare data for the project
- Use AutoML to train and evaluate models for the project
- Deploy the best model and track its performance
Career center
Learners who complete Automatic Machine Learning with H2O AutoML and Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
It is vital for Data Scientists to stay up-to-date on the latest and greatest tools and technologies, including AutoML. This course will help you learn about H2O's AutoML interface and how to use it to automate the process of training and tuning a large selection of models. This will free up your time to focus on other aspects of the data science and machine learning pipeline, such as data pre-processing, feature engineering, and model deployment.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, developing and deploying machine learning models. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the machine learning pipeline, such as data pre-processing, feature engineering, and model deployment.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the data analysis process, such as data exploration, data visualization, and model interpretation.
Business Analyst
Business Analyst
Business Analysts use data to make informed decisions about business strategy. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the business analysis process, such as problem definition, data collection, and data analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make investment decisions. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the quantitative analysis process, such as model selection, risk management, and portfolio optimization.
Statistician
Statistician
Statisticians use data to understand the world around them. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the statistical analysis process, such as data collection, data exploration, and model interpretation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the software development process, such as software design, coding, and testing.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the operations research process, such as problem definition, data collection, and model interpretation.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the financial analysis process, such as financial modeling, risk management, and portfolio optimization.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the market research process, such as survey design, data collection, and data analysis.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the data engineering process, such as data integration, data cleansing, and data visualization.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the database administration process, such as database design, database security, and database performance tuning.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain computer programs. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the computer programming process, such as program design, coding, and testing.
Web Developer
Web Developer
Web Developers design and develop websites. This course will teach you how to use H2O's AutoML interface to automate the process of training and tuning a large selection of models. This will allow you to focus on other aspects of the web development process, such as website design, coding, and testing.
For more career information including salaries, visit:
OpenCourser.com/course/e3o5im/automatic
Reading list
We've selected 15 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
Automatic Machine Learning with H2O AutoML and Python.
This classic textbook provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning algorithms, making it a valuable reference for those seeking a deeper understanding of the underlying theory.
This classic textbook provides a comprehensive overview of artificial intelligence, including machine learning, natural language processing, and computer vision.
Offers a probabilistic approach to machine learning, emphasizing fundamental concepts and providing a solid theoretical foundation.
Offers a comprehensive treatment of supervised and unsupervised learning algorithms, with a focus on practical applications and real-world examples.
Offers a hands-on approach to machine learning using popular Python libraries, providing a solid foundation in model building, evaluation, and deployment.
This cookbook provides practical recipes for implementing machine learning models in Python, covering various algorithms, data preprocessing techniques, and model evaluation methods.
Offers a comprehensive guide to data analysis with Python, covering essential libraries such as NumPy, Pandas, and Matplotlib.
Offers a detailed exploration of TensorFlow, a popular open-source machine learning library, covering its architecture, APIs, and practical applications.
This illustrated guide provides a visual approach to understanding deep learning concepts, making it accessible to readers with varying backgrounds.
Focuses on practical applications of machine learning algorithms, providing real-world examples and case studies to demonstrate their effectiveness.
For those familiar with R, this book offers a comprehensive overview of machine learning concepts and techniques, using R as the primary programming language.
Provides insights into the art of feature engineering, a critical aspect of machine learning projects, covering techniques for data preprocessing, feature selection, and feature transformation.
While not directly related to AutoML, this book provides a solid foundation in deep learning, which related field that is becoming increasingly important in machine learning.
While not directly related to AutoML, this book provides a comprehensive overview of natural language processing techniques, which are becoming increasingly important in machine learning.
This beginner-friendly book introduces fundamental machine learning concepts in a clear and accessible manner, making it suitable for those with limited prior knowledge.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/e3o5im/automatic
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Ideal for learners with a Python, machine learning, and scikit-learn foundation
Focuses on data science and machine learning pipeline aspects beyond creating ML models
Best suited for learners based in the North America region
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