Data for Machine Learning
About this course
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.
83/100
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- What the provider tells you
- 32/45
- Who stands behind it
- 35/35
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- 16/20
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What you'll learn
- Understand the critical elements of data in learning, training, and operation phases
- Identify biases and sources of data
- Implement techniques to improve model generality
- Explain consequences of overfitting and identify mitigation measures
- Implement appropriate test and validation measures
- Demonstrate improvements in model accuracy through feature engineering
- Explore the impact of algorithm parameters on model strength
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