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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.

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Instructors:

English

پښتو, বাংলা, اردو, 3 more

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Practical Machine Learning

This course includes

8 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

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

Machine Learning
Prediction Models
Random Forests
Classification Trees
Cross Validation
Feature Engineering
Model Evaluation
R Programming
Caret Package
Statistical Analysis

This course includes:

4.1 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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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

Brian Caffo
Brian Caffo

4.7 rating

20 Reviews

16,23,662 Students

30 Courses

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

Jeff Leek, PhD
Jeff Leek, PhD

4.7 rating

236 Reviews

16,66,593 Students

32 Courses

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.

Practical Machine Learning

This course includes

8 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

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

3,246 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.