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Learn Logistic Regression, earn certificates with free online courses from Stanford, MIT, University of Michigan, Johns Hopkins and other top universities around the world. Read reviews to decide if a class is right for you.
Explore linear and generalized regression models, covering least squares, multivariable analysis, diagnostics, and applications in data science. Learn to interpret results and select appropriate models.
This course will introduce students to the field by covering stateÂ-of-Âthe-art modeling, analysis and visualization techniques.
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.
Comparative study on AI models for predicting opioid use disorder, showcasing superior performance of transformer-based AI over existing clinical tools and other machine learning approaches.
Comprehensive machine learning series covering fundamental concepts to advanced topics, taught by Andrew Ng. Includes algorithms, neural networks, and reinforcement learning.
Explore deep learning intuition with Stanford experts, covering logistic regression, face verification, neural architectures, and advanced applications in computer vision and image processing.
Extend simple regression to predict outcomes using multiple variables. Practice interpreting data and performing calculations on real studies in public health and biostatistics.
Learn to apply simple regression methods in public health, including linear, logistic, and Cox regression. Practice interpreting data and performing calculations using real-world examples.
Analyze binary outcomes, compare proportions, fit logistic regressions, make predictions, and assess model quality using R. Gain practical skills for health data analysis through lectures and guided coding.
Learn to forecast sports game outcomes using Python, focusing on logistic regression modeling with team expenditure data. Apply and evaluate models for EPL, NBA, and NHL, exploring betting odds and analytics.
Learn to organize, visualize, and analyze health data using R for statistical modeling, linear regression, and logistic regression to make data-informed decisions in public health.
This course presents real-world examples in which quantitative methods provide a significant competitive edge that has led to a first order impact on some of today's most important companies.
Learn how to use classification predictive models to solve business problems involving non-numeric data.
Learn what machine learning is and the steps involved in building and evaluating models. Gain in demand skills needed at businesses working to solve challenges with AI.
Master R programming fundamentals, data manipulation, visualization, and predictive modeling while building practical skills for analyzing and transforming data using powerful statistical tools.
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