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Who should Attend?
KnowledgeHut Experience
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Instructor led Live Training

  • 40 hours Live Sessions
  • 80+ hours MCQs and Assignments
  • 10 Live Projects
  • 35 hours of Hands-on with R for Machine Learning
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One To One Training

Get the benefits of personalized training with your own trainer

Team/Corporate Training
Customizable curriculum and schedules

Description

Alexa Echo, Amazon’s innovative drone Prime Air, and their amazing retail experience Amazon Go are just the tip of the iceberg. Machine learning is on the brink of new adventures, and is among the most sought after sectors for intelligent professionals today. Based on the premise that systems can sort out information from data, identify patterns and make informed decisions without explicit human intervention, machine learning is poised to reinvent our lives as we know it.

KnowledgeHut brings you a comprehensive course that will help you go from basic to advanced concepts in Machine Learning using R, the language that was built by statisticians, for statisticians. Learn to build systems that learn from experience, and exploit data to create simple predictive models of the world. Machine Learning with R looks into Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning, and carries out a comparison of each using R libraries. You will master not only the theory but also see how it is applied in the industry by learning to build predictive models using Machine Learning techniques.

Machine Learning is immensely exciting and creative, and those who have a deep understanding of this smart technology are well equipped to embark on one of the most lucrative careers of this age. Get started on creating innovation that is powered by new-age thinking; become a part of the Machine Learning revolution today!

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What You Will Learn

1. Statistical Learning

Understand the behavior of data as you build significant models

2. R for Machine Learning

Learn about the various libraries offered by R to manipulate, preprocess and visualize data

3. Fundamentals of Machine Learning

Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning

4. Optimization Techniques

Learn to use optimization techniques to find the minimum error in your machine learning model

5. Machine Learning Algorithms

Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail

6. Build Models

Implement algorithms and R libraries such as CRAN-R in real world scenarios

7. Dimensionality Reduction

Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction

8. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance

9. Recommendation systems

Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics

9. Recommendation systems

Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics

1. Statistical Learning

Understand the behavior of data as you build significant models

2. R for Machine Learning

Learn about the various libraries offered by R to manipulate, preprocess and visualize data

3. Fundamentals of Machine Learning

Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning

4. Optimization Techniques

Learn to use optimization techniques to find the minimum error in your machine learning model

5. Machine Learning Algorithms

Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail

6. Build Models

Implement algorithms and R libraries such as CRAN-R in real world scenarios

7. Dimensionality Reduction

Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction

8. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance

9. Recommendation systems

Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics

1. Statistical Learning

Understand the behavior of data as you build significant models

Machine Learning with R Prerequisites
  • Elementary programming knowledge 
  • Familiarity with statistics

Who should Attend?

Those interested in learning ML algorithms for real life business problems
Software or Data Engineers interested in learning quantitative analysis and ML

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.