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Learn Predictive Modeling, earn certificates with free online courses from Stanford, MIT, University of Pennsylvania, University of Michigan and other top universities around the world. Read reviews to decide if a class is right for you.
Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in Python
Explore machine learning's technical aspects, from foundational principles to advanced methods. Learn to avoid pitfalls, interpret models, and evaluate performance while gaining insights into cutting-edge techniques and ethical considerations.
Learn to create quantitative models for informed decision-making in complex, uncertain situations. Develop skills in optimization, risk management, and predictive modeling to make data-driven choices in various business scenarios.
This course will teach you how to start from scratch in understanding and paying attention to what is important in the data and how to answer questions about data.
A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning
Learn to create stories, plans, digital boardroom, analytic apps, and make predictions in SAC. Data source included.
Expand your data analytics skills with hands-on projects using Jupyter Notebook, Python, and Tableau. Prepare for senior roles through advanced topics like machine learning and predictive modeling.
Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks
Analytics /Machine Learning / Data Science: Statistical / Econometrics foundation, SAS Program details, Modeling demo
Enhance predictive and statistical modeling skills to drive data-informed business outcomes. Learn hypothesis testing, regression modeling, and logistic regression using SAS software.
People Analytics & Analytical Process to Human Capital | XL & Open Source Data Mining & ML Tools | 6 Data Assignments
Learn to evaluate regression models using training and test sets in R, mastering data division, MSE calculation, visualization with ggplot, and advanced R programming techniques for model performance assessment.
Explore supervised machine learning regression techniques, from linear models to regularization methods, with hands-on practice in error metrics, data splitting, and model selection for continuous outcome prediction.
You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work
Get ready for ASQ & IASSC Six Sigma Black Belt Certification BoK Topic | Get a Primer on Predictive Modeling
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