This course is part of the Data Science Specialization
Practical Machine Learning
Offered By
What you will learn
- Describe machine learning methods such as regression or classification trees
- Explain the complete process of building prediction functions
- Understand concepts such as training and tests sets, overfitting, and error rates
- Use the basic components of building and applying prediction functions
Skills you will gain
Syllabus - What you will learn from this course
1
Week 1: Prediction, Errors, and Cross Validation
2
Week 2: The Caret Package
3
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
4
Week 4: Regularized Regression and Combining Predictors
34%
83%
14%
Top Reviews
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
Instructors
Jeff Leek, PhD
Associate Professor, Biostatistics
Roger D. Peng, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, BiostatisticsAbout Johns Hopkins University
About the Data Science Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
