One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Offered By
Johns Hopkins UniversityAbout this Course
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Flexible deadlines
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Shareable Certificate
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Approx. 9 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Skills you will gain
- Random Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 9 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
Syllabus - What you will learn from this course
2 hours to complete
Week 1: Prediction, Errors, and Cross Validation
2 hours to complete
9 videos (Total 73 min), 4 readings, 1 quiz
2 hours to complete
Week 2: The Caret Package
2 hours to complete
9 videos (Total 96 min)
1 hour to complete
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
1 hour to complete
5 videos (Total 48 min)
3 hours to complete
Week 4: Regularized Regression and Combining Predictors
3 hours to complete
4 videos (Total 33 min), 2 readings, 3 quizzes
Reviews
- 5 stars66.43%
- 4 stars22.36%
- 3 stars6.90%
- 2 stars2.51%
- 1 star1.77%
TOP REVIEWS FROM PRACTICAL MACHINE LEARNING
by UYNov 16, 2016
Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.
by ACMay 31, 2021
A well descriptive experience for this subject; really steps into how to handle information and how to extract info from them. You need to be prepared with Regression Models, it's the base of it.
by JPJun 24, 2017
Awesome course. Would recommend it, but only to those who have a bit of stats and R background. This definitely helped me get a solid enough understanding of using R for machine learning.
by JCJan 16, 2017
excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!
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