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
- 5 stars66.42%
- 4 stars22.37%
- 3 stars6.90%
- 2 stars2.52%
- 1 star1.77%
TOP REVIEWS FROM PRACTICAL MACHINE LEARNING
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.
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.
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.
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.
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