Machine Learning with R Certification

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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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

About Course

Why Machine Learning as a career?

Benefits of Enrolling KAE Education Machine Learning Certification Course

World Class Instructors

Hands-on Learning

Comprehensive Curriculum

Capstone Project

Certification

Internship

Career Support

Future Career Prospects

Industry Applications

Tools Covered

8-in-1 Learning Excellence with KAE Education

Students Profile

Sample Certificate

Fill this form to know more:

What Will You Learn?

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

Data Cleaning and Transformation

Handling Missing Data

Exploratory Data Analysis (EDA) in R

Feature Scaling and Normalization

Module 3: Supervised Learning with R

Linear Regression in R

Logistic Regression in R

Decision Trees and Random Forests

Support Vector Machines (SVM) in R

Module 4: Unsupervised Learning with R

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Association Rule Mining with R

Module 5: Model Evaluation and Hyperparameter Tuning

Model Evaluation Metrics

Cross-Validation Techniques

Hyperparameter Tuning in R

Fine-Tuning Machine Learning Models

Module 6: Capstone Project

Real-world Machine Learning Project

Application of Concepts Learned

Project Presentation and Discussion

Module 7: Advanced Topics and Applications

Feature Engineering in R

Time Series Analysis with R

Text Mining and Natural Language Processing (NLP) in R

Introduction to Deep Learning with R

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