Practical Machine Learning on H2O
Master machine learning with H2O platform - from fundamentals to advanced techniques, including deep learning and AutoML.
Master machine learning with H2O platform - from fundamentals to advanced techniques, including deep learning and AutoML.
This comprehensive course teaches practical machine learning using the H2O platform. Students learn to implement various algorithms including linear models, random forests, GBMs, and deep learning. The curriculum covers both supervised and unsupervised learning techniques, with hands-on experience in model evaluation, optimization, and deployment. Even those with limited mathematical background can learn to create effective machine learning models using H2O's powerful tools.
4.5
(74 ratings)
8,554 already enrolled
Instructors:
English
Powered by
What you'll learn
Master core machine learning algorithms using H2O
Implement supervised and unsupervised learning techniques
Build and optimize deep learning models
Understand model evaluation and selection methods
Handle data preprocessing and manipulation
Deploy models using POJO and MOJO
Skills you'll gain
This course includes:
333 Minutes PreRecorded video
20 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 6 modules in this course
This course provides a thorough introduction to machine learning using the H2O platform. Through six comprehensive modules, students learn to implement various algorithms and techniques, from basic supervised learning to advanced deep learning methods. The curriculum includes practical applications of linear models, tree-based methods, neural networks, and unsupervised learning approaches. Students gain hands-on experience with model building, evaluation, and deployment using H2O's suite of tools.
H2O AND THE FUNDAMENTALS
Module 1 · 4 Hours to complete
Trees And Overfitting
Module 2 · 3 Hours to complete
LINEAR MODELS AND MORE
Module 3 · 3 Hours to complete
Deep Learning
Module 4 · 4 Hours to complete
UNSUPERVISED LEARNING
Module 5 · 2 Hours to complete
Everything Else!
Module 6 · 5 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Veteran Software Developer and Data Science Author
Darren Cook has established himself as a versatile technology expert with over 20 years of experience spanning software development, data analysis, and technical direction. His diverse portfolio includes developing financial trading systems, natural language processing solutions, data visualization tools, and PR websites for major global brands. His technical expertise encompasses multiple programming languages including R, C++, PHP, JavaScript, and Python, demonstrating his adaptability across different technology stacks. As an author, he has contributed significantly to the field through two O'Reilly publications, including the comprehensive H2O book, sharing his knowledge with the broader technology community. Currently working at QQ Trend, a data analysis and data products company, he continues to apply his extensive experience in creating innovative data solutions. His career exemplifies the evolution of technology over two decades, from traditional software development to modern data science applications.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.5 course rating
74 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.