Introduction to NLP and Text Mining
This course will teach you the essential techniques of text mining, understood here as the extension of data mining’s standard predictive methods to unstructured text.
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This course will teach you the essential techniques of text mining, understood here as the extension of data mining’s standard predictive methods to unstructured text.
This course will teach you the algorithms, techniques and software used in natural language processing (NLP).
A predictive modeling practicum for the predictive analytices course program.
This course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.
This course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling.
This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
This course, with a focus on R, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
This course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.
Public and corporate concern about bias and other unintended harmful effects resulting from data science models has resulted in greater attention to the ethical practice
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