返回到 Data for Machine Learning

4.7

星

18 個評分

•

3 條評論

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:
Understand the critical elements of data in the learning, training and operation phases
Understand biases and sources of data
Implement techniques to improve the generality of your model
Explain the consequences of overfitting and identify mitigation measures
Implement appropriate test and validation measures.
Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering.
Explore the impact of the algorithm parameters on model strength
To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).
This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

篩選依據：

創建者 Emilija G

•Jan 09, 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

創建者 Miguel A S M

•Dec 01, 2019

What is different about this course is its focus of ML applied to the real world.

創建者 Abdullah A

•Dec 24, 2019

the course is very powerful and I have jump to higher level regarding data wrangling and how to deal with data. the assessment have some error which can be fixed easily