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Machine Learning Using R Training

Rated #1 Recoginized as the No.1 Institute for Machine Learning Using R course in chennai

Elevate your career with the Machine Learning Using R course in Chennai. Gain in-depth expertise through interactive sessions and real-world projects, equipping you with the skills to implement machine learning.

Our Machine Learning Using R Training in Chennai offers hands-on experience with essential tools like RStudio, Caret, TensorFlow for R, and Shiny. Get personalized mentorship, earn your Machine Learning Using R certification, and benefit from expert career guidance to land top roles in this fast-growing industry.

  • Enhance your skills and accelerate career growth
  • Enroll now in Machine Learning Using R online training
  • Over 300+ hiring partners and 12,000+ successful graduates
  • The industry-aligned curriculum at competitive pricing with job assistance
  • Comprehensive training with real-world Machine Learning Using R projects
  • Job-focused Machine Learning Using R training with placement-dedicated interview preparation
  • Join the Best Machine Learning Using R Training Institute to Master Data Science and Analytics.
  • Our Machine Learning Using R Course Covers Data Preprocessing, Regression, Classification.
  • Gain Hands-on Experience by Working on Real-world Projects Guided by Certified Experts.
  • Receive Expert Guidance in Crafting a Winning Resume and Excelling in Job Interviews.

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Enhance Your Expertise with AI and Deep Learning Training

  • Our AI and Deep Learning Training, available both online and in classrooms, provides comprehensive insights into machine learning and deep learning principles.
  • Through interactive labs, industry-aligned projects, and hands-on exercises, participants gain practical experience with neural networks, CNNs, RNNs, tensors, model building.
  • Our dedicated career support team guides learners in resume preparation, interview preparation, and personalized mentorship.
  • Stay ahead in the AI landscape by mastering deep learning techniques, optimizing model performance, leveraging Python, and applying practical machine learning and AI workflows.
  • The curriculum aligns with current industry standards and is regularly updated with the latest tools, frameworks, and real-world AI applications.
  • Core topics include AI and deep learning fundamentals, neural network design, CNNs, RNNs, advanced deep learning architectures, model evaluation, optimization methods.
  • Upon completing the program, learners are prepared for roles such as AI Engineer, Machine Learning Engineer, Deep Learning Specialist, Data Scientist, or Computer Vision/NLP Engineer.

What You'll Learn From Machine Learning Training

Master the key concepts of Machine Learning Using R Training, including data analysis, predictive modeling, and algorithm implementation.

Gain a strong understanding of statistical techniques, data preprocessing, and model evaluation to build accurate and reliable machine learning solutions.

Get hands-on experience with real-world datasets, case studies, and projects to bridge the gap between theory and practical applications.

Learn essential R programming skills, including data visualization, feature engineering, and automation for effective machine learning workflows.

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550+ Students Placed Every Month!

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Other Categories Placements
  • Non-IT to IT (Career Transition) 2371+
  • Diploma Candidates3001+
  • Non-Engineering Students (Arts & Science)3419+
  • Engineering Students3571+
  • CTC Greater than 5 LPA4542+
  • Academic Percentage Less than 60%5583+
  • Career Break / Gap Students2588+
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Upcoming Batches For Classroom and Online

Weekdays
02 - Feb - 2026
08:00 AM & 10:00 AM
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Who Should Take a Machine Learning Training

IT Professionals

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Job Roles For Machine Learning Course

R Programmer

Data Analyst

ML Engineer

Statistical Modeler

Data Scientist

Predictive Analytics Specialist

Research Analyst

R ML Solutions Architect

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What’s included ?

Convenient learning format

📊 Free Aptitude and Technical Skills Training

  • Learn basic maths and logical thinking to solve problems easily.
  • Understand simple coding and technical concepts step by step.
  • Get ready for exams and interviews with regular practice.
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🛠️ Hands-On Projects

  • Work on real-time projects to apply what you learn.
  • Build mini apps and tools daily to enhance your coding skills.
  • Gain practical experience just like in real jobs.
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🧠 AI Powered Self Interview Practice Portal

  • Practice interview questions with instant AI feedback.
  • Improve your answers by speaking and reviewing them.
  • Build confidence with real-time mock interview sessions.
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🎯 Interview Preparation For Freshers

  • Practice company-based interview questions.
  • Take online assessment tests to crack interviews
  • Practice confidently with real-world interview and project-based questions.
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🧪 LMS Online Learning Platform

  • Explore expert trainer videos and documents to boost your learning.
  • Study anytime with on-demand videos and detailed documents.
  • Quickly find topics with organized learning materials.
 

Curriculum

Syllabus of Machine Learning Using R Training
Module 1: Introduction to R and Machine Learning
  • Learn R programming environment setup and RStudio interface
  • Understand data types, vectors, lists, matrices, and data frames
  • Perform basic data manipulation and cleaning
  • Explore summary statistics and descriptive analytics
  • Introduction to supervised and unsupervised learning concepts
  • Familiarity with R packages like tidyverse and caret
Module 2: Data Preprocessing and Cleaning
  • Handle missing values using R functions
  • Perform data normalization and scaling
  • Encode categorical variables using factors
  • Detect and treat outliers in datasets
  • Use R packages such as dplyr and data.table
  • Split datasets into training and testing sets
  • Explore feature selection techniques
Module 3: Data Visualization Using R
  • Create basic plots like bar charts, histograms, and boxplots
  • Visualize relationships with scatter plots and line charts
  • Use ggplot2 for advanced data visualization
  • Customize plots with themes, colors, and labels
  • Plot correlation matrices and heatmaps
  • Explore distribution of features with density plots
  • Visualize categorical data using pie and stacked charts
Module 4: Regression Techniques in R
  • Understand linear regression and multiple regression concepts
  • Implement regression models using lm() function
  • Evaluate models using R², RMSE, and MAE metrics
  • Check assumptions of regression models
  • Explore stepwise regression and model selection
  • Learn polynomial and logistic regression basics
  • Use visualization to interpret regression outputs
Module 5: Classification Techniques
  • Understand logistic regression for binary classification
  • Implement decision trees using rpart package
  • Explore k-nearest neighbors (KNN) algorithm
  • Learn Naive Bayes classification in R
  • Use confusion matrix and accuracy metrics
  • Perform cross-validation for model evaluation
  • Explore ROC curve and AUC for performance analysis
Module 6: Clustering and Unsupervised Learning
  • Understand k-means clustering and implementation in R
  • Perform hierarchical clustering and dendrogram analysis
  • Explore DBSCAN clustering algorithm
  • Calculate silhouette score and cluster validation
  • Standardize and normalize data for clustering
  • Use factoextra and cluster packages for visualization
  • Explore principal component analysis (PCA) for dimensionality reduction
Module 7: Association Rule Mining Objectives:
  • Understand market basket analysis and its applications
  • Implement Apriori algorithm using arules package
  • Generate association rules with support, confidence, and lift
  • Filter and sort rules for meaningful insights
  • Visualize association rules with arulesViz
  • Apply rule mining to real-world datasets
Module 8: Time Series Analysis
  • Understand components of time series: trend, seasonality, noise
  • Perform decomposition using stats package
  • Implement moving average and exponential smoothing techniques
  • Use ARIMA models for forecasting
  • Evaluate models using forecast accuracy metrics
  • Visualize time series trends and predictions
  • Explore autocorrelation and partial autocorrelation plots
  • Handle missing values and outliers in time series data
Module 9: Model Evaluation and Optimization
  • Split datasets into training, testing, and validation sets
  • Use k-fold cross-validation for model assessment
  • Implement grid search and parameter tuning in R
  • Evaluate classification and regression models using metrics
  • Detect overfitting and underfitting issues
  • Use ROC curves and confusion matrices for evaluation
  • Compare multiple models to select the best-performing one
  • Apply regularization techniques like Lasso and Ridge
Module 10: Advanced Machine Learning Techniques
  • Implement Random Forest and Gradient Boosting models
  • Learn Support Vector Machines (SVM) for classification
  • Explore ensemble learning techniques in R
  • Introduction to neural networks with nnet package
  • Use feature engineering to enhance model performance
  • Apply dimensionality reduction techniques like PCA
  • Explore boosting algorithms like XGBoost in R
  • Evaluate advanced models using precision, recall, and F1-score
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Course Objectives

  • R programming fundamentals and data structures
  • Data preprocessing, cleaning, and feature engineering
  • Data visualization using ggplot2 and other libraries
  • Supervised learning: regression, classification, and model evaluation
  • Unsupervised learning: clustering, PCA, and association rules
  • Advanced algorithms: Random Forest, Gradient Boosting, SVM, and neural networks
  • Time series forecasting and optimization techniques
    The scope of Machine Learning Using R is growing rapidly across industries. Data-driven decision-making and predictive analytics are becoming essential in sectors like finance, healthcare, retail, and IT. Organizations are increasingly adopting R for statistical modeling, machine learning, and data visualization. As more businesses rely on intelligent insights, professionals skilled in R and machine learning are expected to remain in high demand globally.
  • Gain hands-on experience with real-world datasets
  • Learn both statistical analysis and predictive modeling
  • Build advanced machine learning and deep learning skills
  • Enhance problem-solving and decision-making abilities
  • Improve career prospects in data science, AI, and analytics
Enrolling in this course equips learners with the ability to analyze, model, and interpret complex data. It provides a foundation in R programming and teaches practical machine learning techniques. By learning this course, professionals can confidently solve real-world business problems, optimize processes, and make data-backed decisions. The skills are widely applicable across multiple industries, making it a valuable investment in career growth.

Students learn to use several R tools and libraries:

  • RStudio for development and coding environment
  • tidyverse for data manipulation and cleaning
  • caret for model building and evaluation
  • randomForest and xgboost for advanced algorithms
  • ggplot2 for visualization
  • nnet for neural networks
  • forecast for time series analysis
Learners should have a basic understanding of mathematics, statistics, and logical reasoning. Familiarity with programming in any language is helpful but not mandatory. The course starts with foundational R concepts, so beginners can also join. A curiosity for data analysis and problem-solving is the only essential requirement to benefit fully from the training.
Students work on datasets from multiple industries such as finance, healthcare, and e-commerce. Projects include predicting customer behavior, forecasting sales, analyzing sentiment, and detecting fraud patterns. Through these exercises, learners apply regression, classification, clustering, and time series analysis. This hands-on practice reinforces theoretical concepts and develops practical skills applicable to real business scenarios.

How promising is a career in Machine Learning Using R?

Machine Learning Using R offers excellent career opportunities due to the growing need for data-driven solutions. Companies in IT, healthcare, finance, and research look for professionals who can extract actionable insights from data. Roles like data scientist, machine learning engineer, and analytics consultant are in high demand. Skills learned in R provide a competitive edge and are relevant for both startups and multinational organizations.

What learning materials are provided in the Machine Learning Using R Course?

  • Video tutorials and recorded lectures for each module
  • Sample datasets and coding scripts for practice
  • eBooks and reference guides on machine learning concepts
  • Cheat sheets and library documentation for R functions
  • Interactive exercises to reinforce practical knowledge

Which industries commonly hire professionals with Machine Learning Using R skills?

Industries like IT, finance, healthcare, e-commerce, research, and manufacturing actively hire R professionals. These skills are used for predictive analytics, business intelligence, customer insights, risk management, and optimization. Startups and large enterprises both rely on machine learning specialists to gain competitive advantages, making the demand for trained professionals consistently high.

What key skills will learners acquire from Machine Learning Using R program?

  • Data manipulation and preprocessing in R
  • Visualization techniques to interpret datasets
  • Implementing supervised and unsupervised machine learning algorithms
  • Building predictive models with Random Forest, SVM, and Gradient Boosting
  • Time series forecasting and model evaluation
  • Feature engineering, dimensionality reduction, and advanced analytics
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Overview of Machine Learning Using R Certification Training

Our Machine Learning Using R Course in Online and Classroom is designed for freshers to learn data analysis, predictive modeling, and real-world machine learning techniques easily. This Machine Learning Using R Certification Course helps you gain practical skills in R programming, data visualization, and machine learning algorithms. You will get hands-on experience with datasets and exercises to strengthen your understanding. The program also includes Machine Learning Using R Internships opportunities to apply your knowledge in real scenarios. A dedicated 30-day placement preparation program is included, which focuses on resume building, mock interviews, and soft skills training to boost your confidence.


Additional Info

Key Roles and Responsibilities of Machine Using R Professions:

  • Data Scientist : A Data Scientist analyzes complex datasets to extract actionable insights and inform business decisions. They use R to clean, preprocess, and visualize data, as well as build predictive and statistical models. Understanding patterns and trends in data helps in optimizing operations and strategies. They often collaborate with cross-functional teams to implement data-driven solutions.
  • Machine Learning Engineer : A Machine Learning Engineer develops and deploys machine learning models into production environments. They implement algorithms using R and ensure models are optimized for accuracy and performance. Collaboration with software engineers and data teams is crucial to integrate solutions effectively. Monitoring model performance and retraining with new data is a key responsibility.
  • Data Analyst : A Data Analyst interprets and transforms raw data into meaningful reports and visualizations. They use R to perform statistical analysis, summarize data, and create dashboards for stakeholders. The role involves identifying trends, anomalies, and correlations to support decision-making. Analysts frequently work with structured and unstructured datasets to provide actionable insights.
  • Business Intelligence (BI) Analyst : A BI Analyst leverages R to connect data insights with business strategy. They develop analytical reports, performance metrics, and visual dashboards to guide executives. Machine learning models are applied to forecast trends, customer behavior, and market patterns. Collaborating with data engineers and business managers ensures insights are accurate and relevant.
  • Research Analyst (Data/AI) : A Research Analyst studies data to generate new models, insights, or solutions using machine learning techniques. They explore datasets, test hypotheses, and implement algorithms in R to identify patterns or predictions. The role often involves documenting findings and presenting them to technical and non-technical teams.

Important Tools Covered in Machine Learning Using R Program:

  • RStudio : RStudio is an integrated development environment used for writing and running R code. It provides a user-friendly interface that helps in coding, debugging, and visualizing data. The tool includes a console, script editor, and workspace for managing datasets. RStudio makes it easy to install and manage R packages required for machine learning. It is widely used by beginners and professionals for data analysis and modeling.
  • tidyverse : tidyverse is a collection of R packages used for data cleaning and manipulation. It helps in organizing, filtering, and transforming data in a simple and readable way. Packages like dplyr and tidyr allow easy handling of large datasets. tidyverse also supports working with different data formats. It is very helpful for preparing data before applying machine learning algorithms.
  • ggplot2 : ggplot2 is a powerful visualization tool used to create charts and graphs in R. It helps in understanding data through bar charts, line plots, scatter plots, and histograms. The tool allows customization of colors, labels, and themes. Visualizing data with ggplot2 makes it easier to find patterns and trends. It is commonly used in machine learning for data exploration and analysis.
  • caret : caret is a machine learning package in R used for building and evaluating models. It supports many algorithms like regression, classification, and clustering. The tool helps in data splitting, model training, and performance evaluation. caret makes it easier to compare multiple models in a structured way. It is useful for beginners to understand the complete machine learning workflow.
  • randomForest : randomForest is an R package used to build random forest models for prediction and classification. It works by combining multiple decision trees to improve accuracy. The tool is effective in handling large datasets and complex problems. randomForest helps in identifying important features in the data. It is widely used because it provides reliable and stable machine learning results.

Essential Skills You’ll Learn in an Machine Learning Using R Training:

  • R Programming Skills : R programming skills help in writing code to analyze and process data. This skill includes working with variables, functions, and data structures in R. It allows handling datasets, performing calculations, and running machine learning algorithms. Learning R makes it easier to use libraries created for data analysis and modeling. These skills form the base for working in machine learning and data science roles.
  • Data Cleaning and Preparation : Data cleaning skills focus on making raw data ready for analysis. This includes removing errors, handling missing values, and correcting data formats. Clean data helps machine learning models work more accurately. Understanding how to prepare data is important before applying any algorithm. This skill is used in almost every real-world machine learning task.
  • Data Visualization and Interpretation : Data visualization skills help in understanding data using charts and graphs. This includes creating plots to identify patterns, trends, and relationships. Visual analysis makes complex data easier to understand. It also helps in explaining insights to others clearly. Good visualization skills support better decision-making in machine learning projects.
  • Machine Learning Model Building : Model-building skills involve creating predictive models using algorithms. This includes working with regression, classification, and clustering techniques. It helps in teaching machines to learn from data and make predictions. Understanding how models are trained and tested improves accuracy. These skills are essential for solving real-world problems using data.
  • Model Evaluation and Improvement : Model evaluation skills help in checking how well a machine learning model performs. This includes measuring accuracy, errors, and reliability of predictions. Improving models by tuning parameters leads to better results. These skills help in choosing the right model for a problem. They ensure machine learning solutions are effective and trustworthy.

Future Scope of Machine Learning Using R Certification Course:

  • Rising Data Careers : Machine Learning Using R continues to grow with the demand for data-driven jobs. Many companies need professionals who can analyze data and build prediction models. R is widely used for statistical analysis and machine learning tasks. It helps organizations understand trends and patterns. This makes R skills valuable for long-term career growth.
  • Healthcare and Life Sciences : R plays an important role in healthcare and life sciences analytics. It is used to analyze patient data, predict diseases, and support medical research. Machine learning models in R help improve treatment planning. Hospitals and research centers rely on data insights. This trend will keep expanding as healthcare becomes more digital.
  • Finance and Risk Analysis : Financial organizations use Machine Learning Using R for risk assessment and fraud detection. R helps analyze transaction data and predict market trends. Machine learning models support better investment decisions. Banks and insurance companies trust R for its accuracy. Demand for R professionals in finance is expected to remain strong.
  • Research and Education : Machine Learning Using R is widely used in research and academic fields. Universities prefer R for data analysis and model testing. Researchers use it to study patterns and make predictions. It supports advanced statistical and machine learning methods. This keeps R relevant in education and innovation.
  • Business Forecasting : Businesses use machine learning with R to forecast sales and customer behavior. R helps analyze past data to predict future outcomes. These insights support planning and decision-making. Companies rely on predictions to improve performance. Business forecasting ensures continued use of R in machine learning.
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Gain Practical Experience With Machine Learning Using R Projects

Project 1
Stock Price Prediction

Build a stock price prediction model using R, quantmod, time series analysis, ARIMA, and data visualization techniques to analyze market trends and predict future stock.

Project 2
Recommendation System

Develop a recommendation system using R, collaborative filtering, association rules, arules package, and similarity metrics to suggest products based on user behavior.

Project 3
Disease Prediction System

Create a disease prediction model using R, caret, classification algorithms, data preprocessing, and feature selection techniques to analyze medical data.

Project 4
Credit Risk Assessment

Make a credit risk assessment model using R, logistic regression, randomForest, data normalization, and evaluation metrics to classify loan applicants based on risk levels.

Project 5
Image Classification (Basic ML)

Develop an image classification model using R, keras, CNN concepts, data preprocessing, and model evaluation techniques to categorize images into different classes.

Enhance Your Career with Machine Learning Using R Placement Support

  • Our Machine Learning Using R Certification Training focuses on developing practical skills and confidence to analyze data, build predictive models, and apply machine learning techniques using R.
  • Learners gain hands-on exposure to R programming, statistical modeling, regression, classification, clustering, data visualization, and predictive analytics.
  • The Career Support Program provides guidance from experienced industry professionals, helping learners practice model creation, performance tuning, and applying machine learning solutions.
  • Personalized career mentoring ensures that Machine Learning Using R skills match opportunities in data science, analytics, machine learning, and statistical analysis roles across industries.
  • Completing the course strengthens resumes by highlighting expertise in R-based machine learning models, data analysis, visualization techniques, and data-driven problem solving.
  • Interactive exercises and expert guidance offer opportunities to understand industry workflows, apply practical techniques, and build professional connections.
  • Dedicated Machine Learning Using R Placement Support helps learners explore job opportunities with leading organizations, boosting career confidence and long-term growth.

Earn Professional Machine Learning Using R Certification

Become certified in Machine Learning Using R through an industry-recognized program designed for both beginners and working professionals. This certification is valued for roles such as Machine Learning Engineer, Data Scientist, Data Analyst, Statistical Analyst, and R Programmer, helping improve career prospects and strengthen professional profiles. The course delivers in-depth training with practical exercises and industry-focused learning activities, enabling participants.

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

Get Certified

a physical version of your officially branded and security-marked Certificate.

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About Our Experienced Machine Learning Using R Trainers

  • Our Machine Learning Using R instructors guide learners through practical, real-world data and machine learning scenarios, providing hands-on support for implementing algorithms.
  • We maintain strong industry connections to help learners explore career paths as Machine Learning Engineers, Data Scientists, Data Analysts, or R Programmers.
  • Our Machine Learning Using R Trainers are seasoned professionals with expertise in applying R for data preprocessing, statistical modeling, predictive analytics, regression, classification, clustering, and model optimization.
  • They bring practical experience from projects involving data visualization, predictive modeling, time series analysis, and R-based machine learning solutions for diverse datasets.
  • The program blends self-paced online lessons, live virtual sessions, personalized guidance, and hands-on exercises in R, ensuring an interactive and effective learning experience.
  • From foundational R programming and machine learning concepts to advanced model building, evaluation, and real-world applications, this course equips learners with the skills.

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ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS .
100% Placements | Get Hired in Top MNC

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    We Offer High-Quality Training at The Lowest Prices.

    Affordable, Quality Training for Freshers to Launch IT Careers & Land Top Placements.

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    Updated and Industry-relevant Course Curriculum With Hands-on Learning.

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    Hands-on projects

    Real-world Projects With Live Case Studies and Collaboration With Companies.

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    Industry-recognized Certifications With Global Validity.

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    Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

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    Strong Ties With Top Tech Companies for Internships and Placements

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    Small Batch Sizes for Personalized Attention.

    Large Batch Sizes With Limited Individual Focus.

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    Lifetime Access Course video Materials in LMS, Online Interview Practice, upload resumes in Placement Portal.

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    Machine Learning Using R Course FAQs

    Looking for better Discount Price?

    Call now: +91-7669 100 251 and know the exciting offers available for you!
    • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
    • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
    • More than 3500+ students placed in last year in India & Globally
    • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
    • 85% percent placement record
    • Our Placement Cell support you till you get placed in better MNC
    • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
      ACTE Gives Certificate For Completing A Course:
    • Certification is Accredited by all major Global Companies
    • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS.
    • The entire Machine Learning Using R training has been built around Real Time Implementation
    • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
    • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
    All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
    No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
    We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

    Why Should I Learn Machine Learning Using R Course At ACTE?

    • Machine Learning Using R Course in ACTE is designed & conducted by Machine Learning Using R experts with 10+ years of experience in the Machine Learning Using R domain
    • Only institution in India with the right blend of theory & practical sessions
    • In-depth Course coverage for 60+ Hours
    • More than 50,000+ students trust ACTE
    • Affordable fees keeping students and IT working professionals in mind
    • Course timings designed to suit working professionals and students
    • Interview tips and training
    • Resume building support
    • Real-time projects and case studies
    Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
    Earn an ACTE globally recognized course completion certificate, gain real-world project experience, receive job support, and enjoy lifetime access to resources.
    We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
    We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Machine Learning Using R batch to 5 or 6 members
    Our courseware is designed to give a hands-on approach to the students in Machine Learning Using R. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
    You can contact our support number at +91 76691 00251 Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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    Job Opportunities in Machine Learning

    More Than 35% of Individuals Favor Machine Learning. Machine Learning Stands Out as One of the Most Popular and Sought-after Technologies in the Tech Industry.

    You can Work as a
    Machine Learning Engineer Data Scientist Machine Learning Research Scientist AI Engineer Deep Learning Engineer Data Analyst Robotics Engineer

    Upcoming In-Demand Jobs
    AI Research Scientist Machine Learning Operations (MLOps) Engineer Natural Language Processing (NLP) Engineer Edge AI Engineer Data Engineer with ML Specialization
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