Machine Learning with R by GreyCampus

Goals

By the end of this course, you will have a solid foundation in machine learning with R and be well-equipped to tackle complex machine-learning challenges using the R programming language. You will learn to implement foundational algorithms, build and optimize models, and utilize feature engineering strategies to extract meaningful insights from data.

Prerequisites

What will students learn in your course?

• Introduction to Machine Learning: Gain a solid understanding of fundamental machine learning concepts and principles.

• Introduction to Support Vector Machines (SVM): Explore the SVM algorithm and its use in high-dimensional spaces for classification.

• Decision Tree vs. Random Forest: Compare and contrast decision trees and random forests, understanding their strengths and weaknesses.

• Evaluation Metrics: Learn various metrics for evaluating machine learning models, including accuracy, precision, recall, F1 score, and ROC-AUC.

• Time Series Analysis: Gain insights into time series data, understand trends, seasonality, and noise, and apply models for forecasting.

What are the requirements or prerequisites for taking your course?

>Basic understanding of R programming, including familiarity with data manipulation and visualization libraries 

>Essential knowledge of linear algebra, calculus, and probability/statistics.

>Basic understanding of data structures and control flow in R.