Prevent Overfitting in Model Training
Overfitting can have significant adverse impacts on the performance and generalization ability of a machine learning model. This course will teach you various techniques to overcome this problem and develop a model that performs well on unseen data.
What you'll learn
Overfitting occurs when a machine learning model learns the training data meticulously, interpreting the noise as a signal, which prevents the model from generalizing with new data.
In this course, Prevent Overfitting in Model Training, you’ll gain the ability to understand the causes of overfitting and learn various strategies to mitigate its risks.
First, you’ll explore what overfitting is, its causes, and the impacts on a machine learning model. Next, you’ll learn strategies like regularization to simplify a complex model and data augmentation to diversify the training data. Finally, you’ll learn how to use cross-validation techniques while working with imbalanced datasets and ensemble methods to improve model robustness.
When you’re finished with this course, you’ll have the skills and knowledge to prevent the overfitting problem needed to develop a high-performing machine learning model.
Table of contents
About the author
I have worked in IT design, development, and architecture for over a decade for some of the top fortune 100 companies. I have designed and architected enterprise applications and developed scalable and portable software. I am a Google Certified Professional Architect. Critical areas where I have worked are architecture and design using Java, ESB, Tomcat, ReactJS, JavaScript, Linux, Oracle, SVN, GIT, and so on, and cloud technologies, including AWS and GCP.