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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
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn to create stories, plans, digital boardroom, analytic apps, and make predictions in SAC. Data source included.
A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
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.
Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks
Master Python for predictive analytics: Create data strategies, develop statistical models, and deploy machine learning models to make accurate predictions for business and research purposes.
Analytics /Machine Learning / Data Science: Statistical / Econometrics foundation, SAS Program details, Modeling demo
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
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
In this course you'll learn how to apply machine learning in the HR domain.
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.
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