The Machine Learning with R course is designed to empower learners with the knowledge and skills necessary to excel in the rapidly evolving field of machine learning. This comprehensive program covers key concepts, tools, and techniques, all centred around the R programming language.  Participants will start with a solid foundation in R and then dive into the world of machine learning, exploring topics such as data preprocessing, feature engineering, model selection, and evaluation. The course provides hands-on experience in implementing popular machine-learning algorithms like regression, decision trees, support vector machines, and clustering methods.

Throughout the course, students will work on real-world projects and datasets, honing their ability to solve complex problems and make data-driven decisions. They will also gain insights into best practices for model evaluation and deployment. By the end of this program, learners will have the confidence and expertise to apply machine learning techniques using R to a wide range of practical applications, making them valuable assets in the fields of data analysis, predictive modelling, and artificial intelligence. This course equips them with the skills needed to tackle challenging data-driven problems and stay competitive in today's data-centric industries.

USD 30,800 /-
USD 36,300 /-

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Course Highlights

Comprehensive Curriculum: The course covers a wide range of machine learning topics, from the fundamentals to advanced techniques, ensuring a deep understanding of the subject.

Hands-On Learning: Emphasis is placed on practical experience, with numerous hands-on projects and exercises to reinforce theoretical concepts.

R Language Proficiency: Gain proficiency in R, a powerful programming language for data analysis and machine learning, and apply it to real-world problems.

Industry-Relevant Content: The course is designed to align with industry needs, ensuring that you learn the most relevant skills for data science and machine learning careers.

Expert Instructors: Learn from experienced instructors who have a wealth of knowledge and practical experience in machine learning and R.

Real Data Sets: Work with real-world datasets to solve actual problems, giving you the skills needed to excel in data-driven decision-making.

Algorithms and Models: Master various machine learning algorithms and models, including regression, classification, clustering, and more.

Data Visualization: Explore data visualization techniques in R to effectively communicate insights from your machine learning models.

Certification: Earn a recognized certification upon course completion, validating your expertise in machine learning with R.

Career Opportunities: Equip yourself with in-demand skills, opening up numerous career opportunities in data science, data analysis, and machine learning roles.


After you complete the course with Skillfloor, you will receive a certification The Machine Learning with R course certification, offered through Skillfloor in collaboration with IABAC, provides comprehensive training in the use of the R programming language for machine learning. Participants will gain expertise in implementing machine learning algorithms, data preprocessing, model evaluation, and building predictive models using R. This certification equips individuals with valuable skills for data analysis and predictive modeling, making them well-prepared for a career in the field of data science and machine learning.



Top 10 Reasons For Choosing this Course

Versatility: R is a powerful and versatile programming language for data analysis, making it an excellent choice for machine learning tasks.

Strong Community: R has a large and active community of data scientists and developers, providing excellent support and resources.

Rich Libraries: R offers a wide range of libraries and packages specifically designed for machine learning and data analysis.

Data Visualization: R excels in data visualization, helping you understand and present your findings effectively.

Statistical Analysis: R's statistical capabilities are well-known, making it suitable for in-depth data analysis.

Industry Demand: Machine learning skills with R are in high demand across various industries, offering great career opportunities.

IABAC Certification: The certification from IABAC adds credibility to your skills, making you stand out in the job market.

Hands-On Experience: A Machine Learning with R course typically includes practical projects, ensuring you gain real-world experience.

Interoperability: R easily integrates with other data analysis tools and languages, enhancing your workflow.

Future-Proofing: As machine learning and data science continue to grow, learning R equips you with a skill set that's likely to remain valuable in the future.

Course Curriculum

  •  Overview of R Programming and its environment
     Basic concepts of Machine Learning
     Supervised and Unsupervised learning algorithms
     Introduction to data preprocessing and cleaning

  •  Understanding linear regression models
     Multiple linear regression models
     Polynomial regression models
     NonLinear regression models
     Model selection and validation

  •  Understanding Classification problems
     Logistic Regression
     KNearest Neighbors (KNN)
     Decision Tree
     Random Forest

  •  Clustering techniques using KMeans algorithm
     Hierarchical clustering
     Principal Component Analysis (PCA)
     tdistributed Stochastic Neighbor Embedding (tSNE)

  •  BiasVariance Tradeoff
     Crossvalidation techniques
     Metrics for model evaluation: accuracy, precision, recall, Fscore
     Ensemble models: Bagging and Boosting Techniques

  •  Deep Learning with R using Keras and TensorFlow
     Natural Language Processing (NLP) with R
     Timeseries analysis with R
     Recommender Systems

  •  Understanding the workflows of Machine Learning projects
     Preparing reports and visualizations
     Deploying machine learning models

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