Advanced Predictive Modelling in R Certification Training

Colleagues, the “Advanced Predictive Modelling in R Certification Training” program will boost your career and earnings potential.  R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. This Certification Training is intended for a broad audience as both, an introduction to predictive models as well as a guide to applying them, covering topics such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction etc. Readers will also be able to learn basics of Statistics, such as Correlation and Linear Regression Analysis. Training modules will equip you in: 1) Basic Statistics in R - introduction to statistics and will conduct best test and exploratory analysis - Covariance & Correlation, Central Limit Theorem, Z Score, Normal Distributions, Hypothesis, 2) Ordinary Least Square Regression 1 - learn basic regression and multiple regression and will learn how to present the same graphically - Bivariate Data, Quantifying Association, The Best Line: Least Squares Method, Regressions, Simple Linear Regression, Deletion Diagnostics and Influential Observations, Regularization, 3) Ordinary Least Square Regression 2 - linear regression and make the model a better fit, make necessary transformation check for over fitting and under fitting and outliers’ identification and treatment, Model fitting using Linear Regression, Performing Over Fitting & Under Fitting, Collinearity, Heteroscedasticity, 4) Logistic Regression - assess the problems related with Linear Probability Model, will be introduced to logistic regression and various uses of the same and its industry usage - Binary Response Regression Model, Linear regression as Linear Probability Model, Problems with Linear Probability Model, Logistic Function and Curve, Goodness of fit matrix, IInteractions Logistic Regression, Multinomial Logit, Interpretation, Ordered Categorical Variable, 5) Advanced Regression - logistic regression and learn about more varied usage of logistic regression on various dataset, Poisson Regression, Model Fit Test, Offset Regression, Poisson Model with Offset, Negative Binomial, Dual Models, Hurdle Models, Zero-Inflated Poisson Models, Variables used in the Analysis, Poisson Regression Parameter Estimates, Zero-Inflated Negative Binomial, 6) Imputation - learn about addressing missing values and how to impute it using various processes, 7) Forecasting 1 - an introduction on forecasting and time series data, 8) Dimensionality Reduction, and 8) Survival Analysis - Churn analysis and Regression on time series data with time component. 

Enroll today (teams & executives are welcome): https://tinyurl.com/2tu4musv 

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“Transformative Innovation” book series for your listening-reading pleasure: 

 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing  (Audible) (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)