Machine Learning with R Certification Course (Live Online)

Categories: AI & Machine Learning
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About Course

Why Machine Learning as a career?

 

Benefits of Enrolling KAE Education Machine Learning Certification Course

 

 

 

 

 

World Class Instructors

Learn from 10+ years experienced industry professionals who bring real-world expertise and insights into the classroom.

 

Hands-on Learning

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.

 

Capstone Project

Experience Industry Capstone Project during the training.

 

Certification

Receive a prestigious Certification, Globally acknowledged as a testament to your ML with R proficiency.

 

Internship

Guaranteed Internship after the successful completion of the course.

 

Career Support

Receive guidance on resume building, Interview preparation, and Job Placement Assistance to help launch a successful career in Data Science.

 


Future Career Prospects

 

 


Industry Applications

ML Industry

 

 

 


Tools Covered

 

 

8-in-1 Learning Excellence with KAE Education

 


Students Profile 

 

 


Sample Certificate

 


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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

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