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Course content

About Machine Learning Course Using R in Sehlen

  • Getting Started
  • R Environment
  • R Packages
  • R Data Types: Vectors
  • R Data Frames
  • List
  • Factor and Matrix
  • R History and Scripts
  • R Functions
  • Errors
  • Introduction to Data Handling
  • Importing the Datasets
  • Checklist
  • Subsetting the Data
  • Subsetting Variable Condition
  • Calculated Fields: ifelse
  • Sorting and Duplicates
  • Joining and Merging
  • Exporting the Data
  • Introduction and Sampling
  • Descriptive Statistics
  • Percentiles and Quartiles
  • Box Plots
  • Creating Graphs and Conclusions
  • Introduction to Data Cleaning and Model Building Cycle
  • Model Building Cycle
  • Data Cleaning Case Study
  • CS Lab Step One: Basic Content of Dataset
  • Variable Level Exploration: Categorical
  • Reading Data Dictionary
  • Step Two Lab: Categorical Variable Exploration
  • Step Three Lab: Variable Level Exploration Continues
  • Data Cleaning and Treatment
  • Introduction and Correlation
  • LBA Correlation Calculation in R
  • Beyond Pearson Correlation
  • From Correlation to Regression
  • Regression Line Fitting in R
  • R Squared
  • Multiple Regression
  • Adjusted R Squared
  • Issue with Multiple Regression
  • Multicollinearity
  • Regression Conclusion
  • Need of Non-Linear Regression
  • Logistic Function and Line
  • Multiple Logistic Regression
  • Goodness of Fit for a Logistic Regression
  • Multicollinearity in Logistic Regression
  • Individual Impact of Variables
  • Model Selection
  • Logistic Regression Conclusion
  • Introduction to Decision Tree and Segmentation
  • The Decision Tree Philosophy & The Decision Tree Approach
  • The Splitting Criterion & Entropy Calculation
  • Information Gain & Calculation
  • The Decision Tree Algorithm
  • Split for Variable & The Decision Tree Lab - Part 1
  • The Decision Tree Lab - Part 2 & Validation
  • The Decision Tree Lab - Part 3 & Overfitting
  • Pruning & Complexity Parameters
  • Choosing Cp & Cross Validation Error
  • Two Types of Pruning
  • Tree Building and Model Selection
  • Conclusion
  • Introduction to Model Selection
  • Sensitivity Specificity
  • Sensitivity Specificity Continued
  • ROC AUC
  • The Best Model
  • Errors
  • Overfitting Underfitting
  • Bias_Variance Tradeoff
  • Holdout Data Validation
  • Ten Fold CV
  • Kfold CV
  • MSCV Conclusion
  • Introduction and Logistic Regression Recap
  • Decision Boundary
  • Non-Linear Decision Boundary NN
  • Non-Linear Decision Boundary and Solution
  • Neural Net Intuition
  • Neural Net Algorithm
  • Neural Net Algorithm Demo
  • Building a Neural Network
  • Local Vs Global Min
  • Digit Recognizer Second Attempt Part 1
  • Digit Recognizer Second Attempt Part 2
  • Lab Digit Recognizer
  • Conclusion
  • Introduction to SVM
  • The Classifier and Decision Boundary
  • SVM - The Large Margin Classifier
  • The SVM Algorithm and Results
  • SVM on R
  • Non-Linear Boundary
  • Kernel Trick
  • Kernel Trick on R
  • Soft Margin and Validation
  • SVM Advantage, Disadvantage, and Applications
  • Lab Digit Recognizer
  • SVM Conclusion
  • Introduction to Bagging, RF, and Boosting
  • Wisdom of the Crowd
  • Ensemble Learning
  • Ensemble Models
  • Bagging
  • Bagging Models
  • Random Forest
  • Random Forest Lab
  • Boosting
  • Boosting Illustration
  • Boosting Lab
  • Conclusion
  • Introduction to Clustering via Segmentation
  • Similarities and Dissimilarities
  • Calculating the Distance
  • Clustering Algorithms - K-means
  • More on K-means
  • Clustering Conclusion
  • Cluster Analysis
  • Why Should You Learn Machine Learning Course Using R in Sehlen Training?

    The annual salary of an Machine Learning Course Using R in Sehlen is $125k.

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    Track Week Days Course Duration Fast Track
    Week Days 40-45 Days 2 Hrs. Per Day Online
    Course Duration 7 Weekends 3 Hrs. Per Day Online
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    Students Reviews

    Read genuine student reviews on ZebLearn’s expert-led SAP training and foreign language courses. Hear success stories from learners who gained certifications, skills, and career growth!

    Sneha Deshmukh
    Business Intelligence Analyst
    Completing the Machine Learning Using R course with Zeblearnindia was a fantastic experience. The course provided a thorough understanding of machine learning algorithms and their practical application in R. The real-world applications and hands-on projects were very helpful. This training has enhanced my skills in building and optimizing machine learning models.
    Ravi Patel
    Research Scientist
    The Machine Learning Using R course from Zeblearnindia was exceptional. The course offered a solid foundation in machine learning concepts and practical skills for using R. The modules on data preparation, model evaluation, and hyperparameter tuning were particularly useful. This course has significantly advanced my expertise in machine learning and data analysis.
    Anjali Rao
    Machine Learning Engineer
    Zeblearnindia’s Machine Learning Using R course was outstanding. The course provided in-depth coverage of machine learning techniques and their application in R. The modules on supervised and unsupervised learning were particularly useful. The hands-on projects and real-world case studies were instrumental in applying these concepts to practical problems.
    Vikram Singh
    Data Scientist
    The Machine Learning Using R course from Zeblearnindia was a game-changer for my career. The course offered a comprehensive introduction to machine learning algorithms and their implementation in R. The practical exercises on model evaluation and hyperparameter tuning were especially valuable. This training has significantly enhanced my machine learning skills and knowledge.
    Arjun Mukherjee
    Data Analyst
    The Machine Learning Using R course at Zeblearnindia was highly insightful. It covered essential machine learning concepts and how to implement them using R. The focus on model evaluation and ensemble methods was particularly beneficial. This course has greatly improved my ability to develop and apply machine learning models in my work.

    Machine Learning Course Using R in Sehlen - Flexible batches for you

    Date Type Schedule Time
    SOLD OUT 26 April 2025 Weekend SAT - SUN (08 Week) 18:00 To 20:00
    Filling Img 1 May 2025 Weekday MON - FRI (08 Week) 08:00 To 10:00
    6 May 2025 Weekend MON - FRI (08 Week) 10:00 To 00:00

    Price  1,20,000

    Now  95,000

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