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.
About this Course
Skills you will gain
Johns Hopkins University
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 stars66.40%
- 4 stars22.38%
- 3 stars6.93%
- 2 stars2.49%
- 1 star1.77%
TOP REVIEWS FROM PRACTICAL MACHINE LEARNING
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
This was my favorite class of the specialization. It was taught very well, and I felt like everything I learned in the previous classes were finally coming together.
Good course to learn machine learning through R. It could be more interested to have the processing speed and accuracy compared with other language, such as python.
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.
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