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.
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..
A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.
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