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.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
- 5 stars66.42%
- 4 stars22.36%
- 3 stars6.91%
- 2 stars2.52%
- 1 star1.77%
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.
Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.
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.
This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..