Model Selection & Boosting | Machine Learning Tutorial | Data Science Tutorial | Edureka Rewind
About this course
This course dives into the mathematical foundations of machine learning with a focus on model selection and boosting techniques. It covers essential topics such as the machine learning workflow, data preparation, model evaluation, hyperparameter tuning, and the impact of different machine learning methodologies. Learners will also explore the trade-offs between model accuracy and interpretability, as well as various resampling techniques and their applications in real-world scenarios.
What you should already know
A basic understanding of machine learning concepts and familiarity with data preprocessing techniques is recommended before taking this course.
What you will learn
By the end of the course, learners can expect to confidently select the appropriate machine learning models, effectively evaluate their performance, and apply boosting techniques to enhance model accuracy.