Advanced Predictive Modelling in R Certification Training

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Advanced Predictive Modelling in R Certification Training

R provides a free and open source environment that is ideal for both learning and delivering predictive modeling solutions. This Certification Course is designed for a broad audience as both an introduction to predictive models and a guide to using them, including subjects such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction, and so on. Readers will also master the fundamentals of statistics, such as correlation and linear regression analysis.

Why This Course

This course is for anybody who wants to learn how to use data to obtain insights and make better business decisions. Accounting, finance, human resource management, marketing, operations, and strategic planning are all areas where the approaches presented are used in corporate organizations.

Why Enroll In Course?

Undertaking Advanced Predictive Modeling in R Certification Training can enhance your data modeling, machine learning, and predictive analytics skills, while providing you with hands-on experience with R programming. This training can also improve your career prospects by differentiating you from other candidates and providing you with a competitive edge in the job market. Overall, this certification can provide you with the skills, knowledge, and recognition needed to advance your career in data science and predictive analytics.

Training Features

Live Interactive Learning

Lifetime Access

24x7 Support

Hands-On Project Based Learning

Industry Recognized Certification

Cloud

Course Curriculum

Basic Statistics in R

Learning Objectives: This programmer will provide you with a basic introduction to statistics as well as effective test and exploratory analytic techniques.

Topics:

Covariance & Correlation
Central Limit Theorem
Z Score
Normal Distributions
Hypothesis

Hands On: Calculating statistical metrics such as mean, median, and mode, as well as creating bespoke visuals for building data understanding in relation to statistical parameters.

Ordinary Least Square Regression 1

Learning Objectives: This session will provide you a quick overview of basic regression and multiple regression, as well as how to illustrate the results graphically

Topics:

Bivariate Data
Quantifying Association
The Best Line: Least Squares Method
The Regressions
Simple Linear Regression
Deletion Diagnostics and Influential Observations
Regularization

Hands On: Ridge and Lasso regression implementation.

Ordinary Least Square Regression 2

Learning Objectives:The purpose of this module is to introduce you to linear regression and help you improve the model's fit by performing essential transformations, checking for overfitting and underfitting, and identifying and treating outliers.

Topics:

Model fitting using Linear Regression
Performing Over Fitting & Under Fitting
Collinearity
What is Heteroscedasticity?

Logistic Regression

Learning Objectives: In this session, you will learn about the challenges associated with the Linear Probability Model, as well as logistic regression and its diverse applications in business.
Topics:

Binary Response Regression Model
Linear regression as Linear Probability Model
Problems with Linear Probability Model
Logistic Function
Logistic Curve
Goodness of fit matrix
All Interactions Logistic Regression
Multinomial Logit
Interpretation
Ordered Categorical Variable

Hands On: Build a logistic regression model to classify the data.

Advanced Regression

Learning Objectives: This lesson will take you further into logistic regression and teach you how to use it on a variety of datasets.
Topics:

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

Hands On: Create ZINB and Hurdle regression model.

Imputation

Learning Objectives: This lesson will teach you how to address missing values and how to impute them using various procedures.

Topics:

Missing Values are Common
Types of Missing Values
Why is Missing Data a Problem?
No Treatment Option: Complete Case Method
No Treatment Option: Available Case Method
Problems with Pairwise Deletion
Mean Substitution Method
Imputation
Regression Substitution Method
K-Nearest Neighbour Approach
Maximum Likelihood Estimation
EM Algorithm
Single and Multiple Imputation
Little’s Test for MCAR

Hands On: Implement KNN model and perform single and multiple imputation

Forecasting 1

Topics:

Need for Forecasting
Types of Forecast
Forecasting Steps
Autocorrelation
Correlogram
Time Series Components
Variations in Time Series
Seasonality
Forecast Error
Mean Error (ME)
MPE and MAPE---Unit free measure
Additive v/s Multiplicative Seasonality
Curve Fitting
Simple Exponential Smoothing (SES)
Decomposition with R
Generating Forecasts
Explicit Modeling
Modeling of Trend
Seasonal Components
Smoothing Methods
ARIMA Model-building

Hands On: Implement Exponential Smoothing and ARIMA model for time series forecasting.

Forecasting 2

Learning Objectives: In this module, you will learn about Seasonality, Trend Analysis and decaying the factors over the time.

Topics:

Analysis of Log-transformed Data
How to Formulate the Model
Partial Regression Plot
Normal Probability Plot
Tests for Normality
Box-Cox Transformation
Box-Tidwell Transformation
Growth Curves
Logistic Regression: Binary
Neural Network
Network Architectures
Neural Network Mathematics

Dimensionality Reduction

Learning Objectives: In this lesson, you will gain a thorough understanding of Dimensionality Reduction and will study and implement a number of the most essential Dimensionality Reduction techniques.

Topics:

Factor Analysis
Principal Component Analysis
Mechanism of finding PCA
Linear Discriminant Analysis (LDA)
Determining the maximum separable line using LDA
Implement Dimensionality Reduction algorithm in R

Learning Objectives: You will learn about Churn analysis and Regression on time series data with a time component in this subject.

Topics:

Time-to-Event Data
Censoring
Survival Analysis
Types of Censoring
Survival Analysis Techniques
PreProcessing
Elastic Net

Hands On: Do PCA preprocessing and implement Elastic Net model.

Survival Analysis

Learning Objectives: You will learn about Churn analysis and Regression on time series data with a time component in this subject.
Learning Objectives: In this module, you will learn about Churn analysis and Regression on time series data with time components.

Topics:

Time-to-Event Data
Censoring
Survival Analysis
Types of Censoring
Survival Analysis Techniques
PreProcessing
Elastic Net

Hands On: Do PCA preprocessing and implement Elastic Net model.

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Certification

CertHippo Certification Process:

After completing the project successfully (as reviewed by a CertHippo expert), you will be given CertHippo Advanced Predictive Modeling expert credential. CertHippo certification is widely recognized in the industry, and we are the chosen training partner for many multinational corporations, including Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mind Tree, BNYMellon, and others. Please be confident.

Online Training FAQs

What if I miss a class?

With CertHippo, you will never miss a lecture! You can select one of two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch

Can I attend a demo session before enrollment?

To maintain Quality Standards, we have a restricted number of participants in a live session. As a result, participation in a live class without enrollment is not feasible. But, you may listen to a sample class recording to get a sense of how the lessons are run, the quality of the teachers, and the degree of engagement in a class.

Who are the instructors?

CertHippo instructors are all industry practitioners with at least 10-12 years of relevant IT experience. These are subject matter experts who have been educated by CertHippo to provide participants with an amazing learning experience.

Course Description

About the course

This course will expose you to some of the most extensively used predictive modeling techniques as well as the underlying ideas behind them. Predictive modeling is growing as a competitive approach in many business areas, and it helps distinguish high-performing firms. Predictive analytics issues are typically solved using models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks. Regression models help us understand the correlations between these variables and how they might be used to make decisions

What are the objectives of this course ?

You will be able to do the following after completing this training:

Understand Basics of Statistics using R
Explain Regression
Understand Simple, Multiple, Advanced and Logistic Regression
Perform model fitting using Linear Regression
Explain What is Heteroscedasticity?
Understand Binary Response Variable and Linear Probability Model
Explain Imputation
Understand Forecasting
Learn Neural Networks
Explain Dimensionality Reduction
Understands the algorithms associated with Dimensionality Reduction
Understand Survival Analysis

Why Learn Advanced Predictive Modeling using R?

This course will introduce you to some of the most often used predictive modeling tools and their underlying concepts. It is intended for anybody interested in utilizing data to obtain insights and make better business decisions. The concepts covered in this course are used in all functional areas of corporate organizations, including accounting, finance, human resource management, marketing, operations, and strategic planning.

Who should go for this course?

The following professionals can take up this course:

Developers aspiring to be a ‘Data Scientist’
Analytics Managers who are leading a team of analysts
‘R’ professionals who want to capture and analyze Big Data
Business Analysts who want to understand Machine Learning (ML) Techniques

What are the prerequisites for this course?

To take this course, you must have a basic understanding of R.