SPSS Modeler is a data mining and text analytics software application. SPSS Statistics is a statistics and data analysis program. With this learning path, begin mastering the features and functionality of SPSS Statistics and SPSS Modeler, including AI and machine learning research and applications.
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Master the basic features and functions of SPSS Statistics.
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Learn the basics of using SPSS in academic research.
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Take a deep dive into using SPSS for ML and AI.
Courses
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1
SPSS Statistics Essential Training2h 24mSPSS Statistics Essential Training
By: Barton Poulson
Learn all the essentials of using SPSS, a statistical software suite for data management and advanced analytics.
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2
Machine Learning & AI Foundations: Linear Regression4h 5mMachine Learning & AI Foundations: Linear Regression
By: Keith McCormick
Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.
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3
Machine Learning and AI Foundations: Classification Modeling2h 5mMachine Learning and AI Foundations: Classification Modeling
By: Keith McCormick
Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.
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4
Machine Learning and AI Foundations: Decision Trees with SPSS1h 27mMachine Learning and AI Foundations: Decision Trees with SPSS
By: Keith McCormick
Establish a strong foundation in ML by exploring the IBM SPSS Modeler and learning about CHAID and C&RT. This course is designed to help expand your data science skills.
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5
Machine Learning and AI: Advanced Decision Trees with SPSS1h 23mMachine Learning and AI: Advanced Decision Trees with SPSS
By: Keith McCormick
Work toward a mastery of machine learning by exploring advanced decision tree algorithm concepts. Learn about the QUEST and C5.0 algorithms and a few advanced topics.
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6
Machine Learning and AI Foundations: Clustering and Association3h 33mMachine Learning and AI Foundations: Clustering and Association
By: Keith McCormick
Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.