Regression in Machine Learning in R Course

Why should you take Regression in Machine Learning in R Course?

• Understand the fundamentals of Regression Analysis and its applications in Machine Learning
• Learn to implement various regression techniques including Linear, Logistic, Polynomial, and Ridge Regression using R
• Gain hands-on experience with real-world datasets and case studies
• Master the art of Feature Engineering and Model Optimization
• Learn how to evaluate model performance using essential metrics like R-squared, RMSE, and AIC
• Develop the ability to handle common challenges such as Overfitting, Multicollinearity, and Data Preprocessing
• Build industry-relevant projects to strengthen your portfolio and improve your job prospects in Data Science and Analytics

Who should take Regression in Machine Learning in R Course?

• Aspiring Data Scientists - Individuals looking to build a strong foundation in Machine Learning and Predictive Analytics using R
• Statisticians and Analysts - Professionals who want to apply regression techniques to real-world data using R programming
• R Programming Enthusiasts - Learners who are familiar with R and want to expand their skills into Machine Learning applications
• Students and Researchers - Those pursuing academic or research projects involving Statistical Modeling and Data Analysis
• Business Intelligence Professionals - Individuals who want to make data-driven decisions by leveraging Regression Models
• Individuals interested in Machine Learning - Beginners in the field of Machine Learning who prefer a statistical and code-based approach to learning regression techniques

Curriculum

• 1. Regression
  • 1.1. Simple Linear Regression
  • 1.2. Multiple Linear Regression
  • 1.3. Logistic Regression

Tools you will learn in Regression in Machine Learning in R Course

Skills You Will Gain:

• Building Simple and Multiple Linear Regression Models
• Implementing Logistic Regression for classification problems
• Performing Polynomial and Ridge Regression for Non-Linear Data
• Preprocessing and cleaning data for regression tasks
• Evaluating Model Performance using metrics like RMSE, R-squared, AIC, and Confusion Matrix
• Identifying and Handling Overfitting, Outliers, and Multicollinearity
• Applying Feature Selection and Cross-Validation Techniques
• Interpreting Regression Output and making data-driven decisions

Tools You Will Learn:

• R programming language
• RStudio IDE
• Tidyverse (including dplyr, ggplot2, readr, among others)
• caret for Machine Learning workflows
• lm(), glm(), and predict() functions for model building
• ggplot2 for creating insightful data visualizations
• Model evaluation packages like Metrics, boot, and car