Machine Learning Project Guidelines

This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to work with machine learning algorithms and models.This course is designed based on a whitepaper and the book “Machine Learning Project Guidelines” written by the author of this course.When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.You will also learn that: There is NO single best algorithm that would work well for all predictive modeling problems And, the factors that determine which algorithm to choose for what type of problem(s) Even simple algorithms may outperform complex algorithms if you know how to handle model errors and refine the models through hyperparameter tuningThroughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 13 sections:IntroductionBusiness UnderstandingData UnderstandingResearchData PreprocessingModel DevelopmentModel TrainingModel RefinementModel EvaluationFinal Model SelectionModel Validation & Model DeploymentML Projects Hands-onML Project Template BuildingML Project 1 (Classification)ML Project 2 (Regression)ML Project 3 (Classification)ML Project 4 (Clustering - KMeans)ML Project 5 (Clustering – RFM Anal