Data for Machine Learning by Coursera

This course is about data and how it is critical to the success of an applied model for machine learning. This course will give learners the skills they need to:

Understanding the data elements is key during learning, training and operation. Recognize biases as well as data sources. You can use techniques to increase the generality of your model. Find mitigation strategies and explain the consequences of overfitting. Validation and testing measures should be used. Demonstrate how feature engineering can increase the accuracy of your model. Learn how algorithm parameters affect model strength. You will need to be familiar with Python programming in order to succeed in this course. Basic knowledge of linear algebra (vectornotation), and statistics (probability, median/mode) are required. This course is part the Applied Machine Learning Specialization that was brought to you through Coursera.