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Learn Polynomial Regression, earn certificates with paid and free online courses from Harvard, Stanford, CU Boulder, Tecnológico de Monterrey and other top universities around the world. Read reviews to decide if a class is right for you.
Explore atomic cluster expansion for hybrid interatomic potentials, covering methodology, applications, and theoretical foundations in quantum mechanics and materials science.
Learn the concepts and techniques that make up the foundation of data science and machine learning.
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.
Master advanced regression techniques from polynomial to elastic net, with hands-on practice using real datasets for predictive modeling and performance optimization.
Master regression modeling techniques to build predictive models using Python, covering multiple linear, polynomial, lasso and ridge regression, along with essential model evaluation metrics and visualization methods.
Master regression analysis techniques to model relationships, prevent overfitting, and make data-driven decisions. Explore linear and polynomial regression, regularization, cross-validation, and ensemble methods through hands-on practice.
Learn how to write and graph functions in R and how to fit polynomials to data sets.
Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.
Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
Where is the International Space Station, Who's On It? Let's Build a Web App to Find out with Python and Data Science
Find out how to use prebuilt Python libraries for predictive analytics and discover insights about the future.
Explore machine learning fundamentals, including hypotheses, data representation, algorithms like regression and classification, and practical implementation using MATLAB.
Comprehensive introduction to machine learning fundamentals using Python and R, covering linear regression, data types, polynomial regression, and NumPy, with hands-on projects and practical applications.
Master nonlinear regression concepts, from polynomial mappings to regularization techniques, while learning practical implementation in Python and understanding cross-validation for model optimization.
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