Machine Learning with R Certification Course (Live Online)

Categories: AI & Machine Learning
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About Course

Sample Certificate Click here

 

Batch Dates (January, 2024 to December, 2024)
January 6-Jan-2024 13-Jan-2024 20-Jan-2024 27-Jan-2024
February 4-Feb-2024 11-Feb-2024 18-Feb-2024 25-Feb-2024
March 3-Mar-2024 10-Mar-2024 17-Mar-2024 24-Mar-2024
April 7-Apr-2024 14-Apr-2024 21-Apr-2024 28-Apr-2024
May 5-May-2024 12-May-2024 19-May-2024 26-May-2024
June 2-Jun-2024 9-Jun-2024 16-Jun-2024 23-Jun-2024
July 7-Jul-2024 14-Jul-2024 21-Jul-2024 28-Jul-2024
August 4-Aug-2024 11-Aug-2024 18-Aug-2024 25-Aug-2024
September 8-Sep-2024 15-Sep-2024 22-Sep-2024 29-Sep-2024
October 6-Oct-2024 13-Oct-2024 20-Oct-2024 27-Oct-2024
November 3-Nov-2024 10-Nov-2024 17-Nov-2024 24-Nov-2024
December 8-Dec-2024 15-Dec-2024 22-Dec-2024 29-Dec-2024

This 32-hour Machine Learning with R Certification Course covers key concepts in supervised and unsupervised learning using the R programming language. The course includes practical coding sessions, case studies, and a capstone project to provide participants with hands-on experience in implementing machine learning algorithms using R.

 

Course Overview:

This course blends theoretical foundations with hands-on practical applications to ensure a holistic understanding of machine learning using R. Led by industry experts, the curriculum covers fundamental concepts, advanced techniques, and real-world projects to equip you with the skills needed to excel in the rapidly evolving field of machine learning.

 

Why Choose the Machine Learning with R Certification Course at KAE Education:

  • Expert-Led Instruction:
    • Learn from industry experts with extensive experience in machine learning and R programming.
  • Hands-On Experience:
    • Gain practical skills through hands-on projects and real-world applications.
  • Comprehensive Curriculum:
    • Cover fundamental and advanced machine learning concepts using the versatile R language.
  • Global Recognition:
    • Receive a prestigious certification, globally acknowledged as a testament to your ML with R proficiency.
  • Career Advancement:
    • Acquire skills that are in high demand across industries, opening new career opportunities.

Enroll now to unravel the world of machine learning with R and empower yourself to navigate the complex landscape of data-driven decision-making.

Prerequisites: Basic understanding of programming concepts is recommended, but the course is designed to accommodate learners with varying levels of expertise.

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What Will You Learn?

  • Foundations of Machine Learning:
  • Definition and types of machine learning.
  • Understanding supervised and unsupervised learning.
  • Application of R in machine learning.
  • R Programming Fundamentals:
  • Basics of R programming: syntax, data types, variables.
  • Data manipulation and visualization in R.
  • Introduction to R packages for machine learning.
  • Supervised Learning with R:
  • Linear Regression and Logistic Regression.
  • Decision Trees and Random Forests.
  • Support Vector Machines (SVM) in R.
  • Unsupervised Learning with R:
  • Clustering techniques: K-Means, Hierarchical Clustering.
  • Dimensionality reduction with Principal Component Analysis (PCA).
  • Association rule mining using the Apriori algorithm.
  • Advanced Machine Learning Topics:
  • Ensemble learning: Boosting and Bagging.
  • Hyperparameter tuning.
  • Model evaluation and validation techniques.
  • Real-World Projects and Capstone Experience:
  • Application of learned concepts in practical scenarios.
  • Project proposal, planning, and implementation.
  • Final project presentation and peer evaluation.

Course Content

Module 1: Introduction to Machine Learning and R Programming

  • Overview of Machine Learning
  • Introduction to R Programming
  • R Environment Setup
  • Basic R Syntax and Data Structures

Module 2: Data Preprocessing with R

Module 3: Supervised Learning with R

Module 4: Unsupervised Learning with R

Module 5: Model Evaluation and Hyperparameter Tuning

Module 6: Capstone Project

Module 7: Advanced Topics and Applications