Practical Machine Learning
Master practical machine learning techniques from prediction to model evaluation using R.Developing Data Products
Master practical machine learning techniques from prediction to model evaluation using R.Developing Data Products
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Specialization or Data Science: Statistics and Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.5
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Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Build and apply prediction functions
Implement cross-validation techniques
Use the caret package effectively
Develop machine learning models
Evaluate model performance
Handle preprocessing and feature creation
Skills you'll gain
This course includes:
4.1 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on practical applications of machine learning, covering the complete process of building prediction functions. Students learn about training and test sets, overfitting, error rates, and various machine learning methods including regression, classification trees, Naive Bayes, and random forests. The curriculum emphasizes hands-on experience with the caret package in R and includes preprocessing, feature creation, algorithm implementation, and model evaluation.
Prediction, Errors, and Cross Validation
Module 1 · 2 Hours to complete
The Caret Package
Module 2 · 2 Hours to complete
Predicting with trees, Random Forests, & Model Based Predictions
Module 3 · 1 Hours to complete
Regularized Regression and Combining Predictors
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Expert in Biostatistics and Neuroinformatics
Brian Caffo, PhD, is a professor in the Department of Biostatistics at the Johns Hopkins University Bloomberg School of Public Health. He earned his PhD in Statistics from the University of Florida in 2001. Specializing in computational statistics and neuroinformatics, he co-created the SMART working group
Chief Data Officer and J Orin Edson Foundation Chair at Fred Hutchinson Cancer Center
Dr. Jeff Leek serves as the Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. He earned his PhD in Biostatistics from the University of Washington and is known for his significant contributions to genomic data analysis and statistical methods for personalized medicine. His research has advanced our understanding of molecular mechanisms related to brain development, stem cell self-renewal, and immune responses to trauma, with findings published in top scientific journals such as Nature and Proceedings of the National Academy of Sciences. Dr. Leek developed a highly acclaimed Data Analysis course for Biostatistics students at Johns Hopkins, which has consistently received teaching excellence awards. He is also recognized for his efforts in creating educational initiatives that leverage data science for public health and economic development, including massive open online courses that have engaged millions worldwide.
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4.5 course rating
3,246 ratings
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