Machine Learning Using R

Learn to create Machine Learning Algorithms using  R


Total Lesson


Total Video


What is Machine Learning ?

Are you fascinated by how Amazon  recommends the products you’ll like or Have you wondered what is the Google algorithm that shows you such accurate search results ?

Machine Learning is behind these innovations. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions.  Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

What is this course about?

Advance your Machine Learning journey and start learning some concepts and techniques that are core to Machine Learning.  BigDataHorizon’s Machine Learning course has been designed by professional Data Scientists to help you learn complex theory, algorithms and coding libraries in a simple way. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications.

What are the Course Objectives?

By the end of this course you will:

  • Learn Machine Learning algorithm using   R
  • Make powerful analysis and predictions
  • Understand and implement different Machine Learning modelsCreate strong added value to your business
  • Use Machine Learning for personal purpose
  • Learn topics like Reinforcement Learning, NLP and Deep Learning
  • Understand which Machine Learning model to choose for each type of problem

Who should do this course? 

  • Professionals interested in Machine Learning.
  • Final Year of Engineering students or Early Professionals who want to start a career in Data Science & Machine Learning.
  • Any data analysts who want to level up in Machine Learning.
  • Professionals looking for career growth in the field of Data Scientist.

What are the System Requirements for this Course?

In order to successfully undergo our course, you’ll need a computer which satisfies minimum system requirements. This includes:

  • A Windows or Mac  computer
  • Internet connectivity
  • Headset, speakers and microphone
  • How Will I Execute the Practical?

To execute the practical, you need to install R studio which will be shown during the course.


Chapter 1 - Data Preprocessing
Different Data Processing Methods
Chapter 2 - Regression Modelling
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Decision Tree Regression
Random Forest Regression
Chapter 3 - Classifications
Logistic Regression
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Chapter 4 - Clustering
Hierarchical Clustering
Chapter 5 - Association Rule Learning
Chapter 6 - Reinforcement Learning
Upper Confidence Bound
Thompson Sampling
Chapter 7 - Natural Language Processing
Bag-of-words model and algorithms for NLP
Chapter 8 - Deep Learning
Artificial Neural Networks
Convolutional Neural Networks
Chapter 9 - Dimensionality Reduction
PCA, LDA, Kernel PCA
Chapter 10 - Model Selection & Boosting
k-fold Cross Validation, Parameter Tuning, Grid Search

Prosenjit De , Praveenkumar Devarajan , Harish Kumar P V , Soumen Saha , Amitabh Biswal

Co Author of BigDataHorizon’s Machine Learning course

Praveenkumar Devarajan
Data Architect at Anthem, Orange County, California

Prosenjit De
Co-Founder of BigDataHorizon

Harish Kumar P V
Data Engineer at Leading MNC in US

Soumen Saha
Big Data - Architect , Designer

Amitabh Biswal
Co-Founder of BigDataHorizon
Alumni of London Business School

What is included

  • Access to Course Discussion Forum
  • High Quality pre recorded course video(Big Data)
  • Virtual Machine Installation Support
  • Support from Experts
  • Live Instructor Led sessions ( for Instructor Led)
  • Topic Quizzes
  • Real-life Case Studies
  • Final Assessment Test
  • Certification


There are no specific prerequisites to be eligible for our Machine Learning Training Course. As an early professional or an engineering student, you can go ahead and enroll for our training course.

Case Studies

Towards the completion of our Machine Learning Training Course, you will work on a live case study using core concepts learnt during the training. Our industry-specific case studies span various industries including Fintech , Insurance , Supply Chain, Human Resource, Healthcare and Retail. You can choose any industry-specific case study to work on.
These are only basic examples of the real-life case studies you can choose to work on. More specifics and details regarding case studies will be revealed to you during your training.


Your doubts and queries regarding our Big Data Training Course – answered!

1. Who will train me? How do you select faculties?

You will be trained by leading industry experts who are highly qualified and certified as instructors. We select faculties with extreme care so that you get the best possible training experience.

2. What are the different training models you offer?

We offer only instructor-led training for Business Analytics Course.

3. What if I miss a class or training session?

When you choose our Machine Learning Training Course, you need not worry about missing a class or training session. We’ll provide you with prerecorded video content which you can access at any time. And, if you miss any instructor session, you can always attend the missed session with any other live batch.

4. How can I enroll for your training?

You can enroll for our Machine Learning Training Course right here on our website

5. How do I pay for the training course?

We accept a variety of payment options, which include online money transfer, credit and debit cards (Visa, Mastercard. You can choose any of these payment options as per your convenience.

6. Do you offer group discounts?

Yes, we do! To find out more about what group discounts we offer on our AWS Cloud Training Course, please get in touch with us.

7. How do I learn more about your training course?