Machine Learning using R Training in Gurgaon, Delhi

Machine Learning using R

Build a Portfolio of 12 Machine Learning Projects using R, SVM, Regression, Unsupervised Machine Learning & More!

Duration : 6 months Classes : 36 Days : Weekdays / Weekends

Overview
Curriculum
Pre-requisite
Review

Unlock the power of predictive analytics with our hands-on course Machine Learning using R. Designed for data professionals, analysts, and researchers, this course offers a practical and intuitive approach to building machine learning models using R's robust ecosystem of packages such as caret, tidymodels, randomForest, and xgboost. Learners will explore the full machine learning pipeline - from data preprocessing and feature engineering to model training, evaluation, and deployment. With real-world datasets and guided projects, participants will gain the skills to solve classification, regression, and clustering problems while mastering techniques like cross-validation, hyperparameter tuning, and ensemble learning. Whether you're looking to automate decision-making, uncover patterns, or forecast outcomes, this course equips you with the tools and confidence to apply machine learning in any domain using R.

You have the data engineering skills; now gain the statistical edge. This course bridges the gap between general-purpose ML frameworks and R's deep statistical ecosystem. We focus on efficiently utilizing R's unique strengths, especially the high-level TidyModels suite, which streamlines tasks that are complex in other environments. Learn to build automated, reproducible ML pipelines that feed directly into R's unparalleled reporting and dashboarding tools (Shiny), allowing you to deliver end-to-end data products quickly and efficiently. Add R-based advanced statistical modeling to your toolkit and elevate your analytical influence.

We focus on building models that are not only accurate but also highly interpretable and statistically sound.

This course blends theory with hands-on practice to help you build intelligent systems that learn and adapt. Whether you're looking to break into AI or sharpen your DL/ML/DS toolkit, this program delivers the skills and confidence to thrive.

Target Audience:-
-Academic Researchers, PhD/Masters Students, and anyone prioritizing model rigor
-Aspiring Data Scientists, Data Analysts seeking to specialize in ML, and Statisticians
-Data Engineers, Python-proficient Data Scientists, BI Developers
-Early - career data scientists looking to build a strong foundation in machine learning
-Recent Graduates in non-CS fields (e.g., Economics, Biology, Business)

Program Outcomes:-
-Understand core machine learning concepts and how they are implemented in R
-Build supervised learning models
-Statistical Ecosystem
-Apply unsupervised techniques
-Use ensemble methods like bagging and boosting
-Deploy models and communicate results using R Markdown and Shiny dashboards

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas

-You are familiar with R Programming, SQL and High School Statistics

....

NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.

NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.

This syllabus provides a structured, module-by-module breakdown of this comprehensive training program focused on participants overall performance, retention, and engagement, covering foundational theory, implementation, best industry practices and advanced techniques in the subject.

Module 1: Foundational Concepts and R for ML
✔ ML Fundamentals
✔ R for Tidy Modeling
✔ Data Splitting and Resampling
✔ Feature engineering and scaling techniques
✔ Performance Metrics

Module 2: Linear Models and Interpretability
✔ Linear Regression
✔ Tidy Linear Modeling
✔ Logistic Regression
✔ Regularization Techniques
✔ Model Evaluation and ROC Curves

Module 3: Tree-Based Models and Ensemble Methods
✔ Decision Trees
✔ Random Forests
✔ Feature Importance
✔ Gradient Boosting Machines (GBMs)
✔ Stacking and Blending (Conceptual)

Module 4: Advanced Pre-processing and Hyperparameter Tuning
✔ Advanced Feature Engineering
✔ Defining Parameter Grids
✔ Cross-validation techniques
✔ Grid search and random search strategies
✔ Avoiding overfitting and improving generalization
✔ Tuning with tune
✔ Workflow Sets and Finalization
✔ Model Deployment and Persistence

Module 5: Unsupervised Learning and Model Interpretation
✔ Clustering (K-Means)
✔ Principal Component Analysis (PCA) for dimensionality reduction
✔ Hierarchical Clustering
✔ Model Explanation (XAI)

Module 6: Advanced Machine Learning Models
✔ Ensemble methods: bagging, boosting, stacking
✔ Gradient boosting with xgboost and LightGBM
✔ Neural networks with nnet and keras in R
✔ Comparing model performance across algorithms

Module 7: Capstone Project
✔ Project Scope
✔ Select a real-world dataset and define a question
✔ Apply full data science workflow: import, wrangle, visualize, model, report
✔ Present findings with code, visuals, and narrative

NB:The curriculum is regularly subjected to updates, reflecting the latest industry trends & current technological advancements.

At Vyom Data Sciences, we aspire to provide the latest curriculum and most recent technology, as a standard component of all our trainings. Experts, with 25+ years of experience from USA, Europe and Australia, bring the best industry practices while designing and executing these trainings. All our trainers are Highly Qualified and Certified in their respective subject areas.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre.

Bhawana

Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.

Harish

Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.