Data for Machine Learning (Coursera)

https://www.coursera.org/learn/data-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 acc

Machine Learning Algorithms: Supervised Learning Tip to Tail (Coursera)

https://www.coursera.org/learn/machine-learning-classification-algorithms

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have

Machine Learning: Algorithms in the Real World (CourseraSpecs)

https://www.coursera.org/specializations/machine-learning-algorithms-real-world

Offered by Alberta Machine Intelligence Institute. Machine Learning Real World Applications. Master techniques for implementing a machine ...

Optimizing Machine Learning Performance (Coursera)

https://www.coursera.org/learn/optimize-machine-learning-model-performance

This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this