Learn in Identifying the collinearity problem with 2 inputs; Function for Scatter diagram and the correlation coefficient values in one visualization; Identifying and removing influential records; Analysis of Diagnostic plots - Cook's Distance, Studentized residual, Bonferroni p - values, hat values; Variance Inflation Factor & Added Variable Plots for identifying the column that needs to be removed from the regression modelling; Multiple R squared value vs Adjusted R squared value; Evaluating the LINE Assumptions using Plots
About this course: This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).
About this course: This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core text mining techniques including text preprocessing, sentiment analysis, and topic modeling help learners be trained to be a competent data scientists. Empowered by bringing lecture notes together with lab sessions based on the y-TextMiner toolkit developed for the class, learners will be able to develop interesting text mining applications.
Starting from Start-ups to Conglomerates every organization is relying on data for decision making. They are looking for skilled manpower who can convert this gigantic data into sleek information for decision making. This course fuses Statistical Analysis with MS Excel because of which you would be able to churn legions of data into meaningful information within no time. Excel has hundreds of built-in functions and Data Analysis Toolpak (Excel Add-in) with which you can run descriptive statistics to predictive analysis with ease.