ML Regression
Master regression by predicting house prices, investigate regularized linear regression, manage extensive feature sets, and employ optimization algorithms to make precise predictions with large datasets.
Description for ML Regression
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Washington
Duration: 22 hours (approximately)
Schedule: Flexible
Pricing for ML Regression
Use Cases for ML Regression
FAQs for ML Regression
Reviews for ML Regression
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML Regression
Become an expert in the field of artificial intelligence. Develop effective strategies for the application of Artificial Intelligence techniques to address business challenges.
This course offers a concise summary of essential multivariate calculus for machine learning, including practical tools, vector calculations, function approximation, and neural network applications, to build confidence for advanced studies.
Commence Your Career in Data Science. A ten-course introduction to data science, devised and instructed by distinguished professors.
Discover the process of identifying machine learning model types, training and deploying predictive models using Azure Machine Learning's automated capabilities, developing regression, classification, and clustering models with Azure Machine Learning Designer, and deploying models seamlessly without scripting.
Besides Python programming and data science fundamentals, the course covers supervised machine learning regression, which includes training models for continuous outcomes, error metrics, Elastic Net, LASSO, Ridge regularization, and data science fundamentals for aspiring data scientists.
Learn to build and train supervised machine learning models for binary classification and prediction tasks using Python with NumPy and scikit-learn libraries.
Learn regression analysis, build prediction functions, and develop public data products.
Featured Tools
Acquire an extensive understanding of reinforcement learning, deep neural networks, clustering, and dimensionality reduction to effectively address real-world machine learning challenges.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.