Data for Machine Learning
Learn the critical elements of data in machine learning and improve your model’s accuracy with this Applied Machine Learning course. Gain skills in data understanding, bias identification, and feature engineering. Enhance your model’s generality and mitigate overfitting. Prior Python programming and basic knowledge of linear algebra and statistics are recommended. Join Coursera and the Alberta Machine Intelligence Institute for this comprehensive specialization.
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.
User Reviews
Be the first to review “Data for Machine Learning”
You must be logged in to post a review.
There are no reviews yet.