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Data for Machine Learning

Data for Machine Learning

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9.3/10 (Our Score)
Product is rated as #10 in category Machine Learning

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. The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta–based research institute that pushes the bounds of academic knowledge and …

Instructor Details

Anna is Senior Scientific Advisor at the Alberta Machine Intelligence Institute (Amii), working to nurture productive relationships between industry and academia. Anna, whose research mainly focused on reinforcement learning, received her Master’s in Computing Science under the supervision of Dr. Richard Sutton, one of the field’s pioneers, and she is currently a PhD candidate working to develop algorithms for real-time learning in dynamic environments. Passionate about making science accessible for all, Anna has developed and taught a wide range of computing science classes through the University of Alberta.

Specification: Data for Machine Learning

Duration

13 hours

Year

2019

Level

Intermediate

Certificate

Yes

Quizzes

Yes

5 reviews for Data for Machine Learning

4.8 out of 5
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  1. Miguel A S M

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

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  2. Abdullah A

    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

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  3. Emilija G

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

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  4. Emil K

    The instructor is great, but please fix the programming assignment! There are so many typos it’s embarassing. Also, the autograder EXPECTS typos in some variable names, so you can’t even pass it if your answers are correct.

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  5. Valerii M

    Nice course!

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