Intro to Hyperparameter Tuning with Python
|
Improve machine learning models with hyperparameter tuning.
About this course
Hyperparameters are values that can be adjusted to improve a Machine Learning model. In this course, you will learn industry standard techniques for hyperparameter tuning, including Grid Search, Random Search, Bayesian Optimization, and Genetic Algorithms.
Notes on Prerequisites
We recommend that you complete Intro to Regularization with Python before completing this course.
Skills you’ll gain
Understand the role of hyperparameters
Improve model performance with tuning
Pick the best tuning method for a model
User Reviews
Be the first to review “Intro to Hyperparameter Tuning with Python”
Related Products
SQL Fluency
Achieve fluency in SQL, focusing on advanced queries, data modeling, and database design techniques.
Discovering Quantum Computing
Learn the basics of quantum computing, including its principles and potential applications in technology.
Programming for Data Science with R
Learn R programming, focusing on data analysis, statistics, and data science applications.
Discovering RPA
Learn about Robotic Process Automation (RPA) and its role in automating business processes.
Introduction to Programming with Python I
Learn the basics of programming using Python, focusing on syntax, data types, and control structures.
Data Structures and Algorithms
Explore essential data structures and algorithms, focusing on problem-solving and coding efficiency.
There are no reviews yet.