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R

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Machine learning is a field which involves in transforming raw data into data of actionable knowledge. The prominence of R …

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Machine learning is a field which involves in transforming raw data into data of actionable knowledge. The prominence of R language is increasing day by day as it is a cross platform and a zero cost statistical tool. Machine learning with R offers a powerful set of methods for quick and easy access of data.
  • Contents
    • Correlation
    • Simple Regression
    • Logistic regression
    • What is segmentation
    • What is a Decision tree
    • Tree validation
    • Over fitting and Under fitting
    • Cross validation
    • Boot strapping
    • Neural networks
    • SVM
    • Random Forest algorithm
    • Boosting
  • After the Completion of This Course, one will have a good hands on machine learning with R programming. R will make the various machine learning technique more simpler.

Course Currilcum

    • Handout – Regression Analysis 00:00:00
    • 1.1 Introduction and Corelation FREE 00:00:00
    • 1.2 LAB Corelation Calculation in R FREE 00:00:00
    • 1.3 Beyond Pearson Corelation 00:00:00
    • 1.4 From Corelation to Regression 00:00:00
    • 1.5 Regression Line Fitting in R 00:00:00
    • 1.6 R Squared 00:00:00
    • 1.7 Multiple Regression 00:00:00
    • 1.8 Adjusted R Squared 00:00:00
    • 1.9 Issue with Multiple Regression 00:00:00
    • 1.10 Multicollinearity 00:00:00
    • 1.11 Regression Conclusion 00:00:00
    • Regression Quiz Unlimited
    • Handout – Logistic Regression in R 00:00:00
    • 2.1 Need of Non-Linear Regression FREE 00:00:00
    • 2.2 Logistic Function and Line FREE 00:00:00
    • 2.3 Multiple Logistic Regression 00:00:00
    • 2.4 Goodness of Fit for a Logistic Regression 00:00:00
    • 2.5 Multicollinearity in Logistic Regression in R 00:00:00
    • 2.6 Individual Impact of Variables in R 00:00:00
    • 2.7 Model Selection in R 00:00:00
    • 2.8 Logistic Regression Conclusion 00:00:00
    • Logistic Regression Quiz Unlimited
    • Handout – Decision Tree in R 00:00:00
    • 3.1 Introduction to Decision Tree & Segmentation FREE 00:00:00
    • 3.2 The Decision Tree Philosophy & The Decision Tree Approach FREE 00:00:00
    • 3.3 The Splitting criterion &Entropy Calculation 00:00:00
    • 3.4 Information Gain & Calculation 00:00:00
    • 3.5 The Decision Tree Algorithm 00:00:00
    • 3.6 Split for Variable & The Decision tree-lab(Part 1) 00:00:00
    • 3.7 The Decision tree-lab(Part 2) & Validation 00:00:00
    • 3.8 The Decision tree -lab (Part3) & Overfitting 00:00:00
    • 3.9 Pruning & Complexity Parameters 00:00:00
    • 3.10 Choosing Cp & Cross Validation Error 00:00:00
    • 3.11 Two types of Pruning 00:00:00
    • 3.12 Tree Building & Model Selection-Lab 00:00:00
    • 3.13 Conclusion 00:00:00
    • Decision Trees Quiz Unlimited
    • Handout – Model Selection and Cross Validation in R 00:00:00
    • 4.1 Introduction to Model Selection FREE 00:00:00
    • 4.2 Sensitivity Specificity FREE 00:00:00
    • 4.3 Sensitivity Specificity Continued 00:00:00
    • 4.4 ROC AUC 00:00:00
    • 4.5 The Best Model 00:00:00
    • 4.6 Errors 00:00:00
    • 4.7 Overfitting Underfitting 00:00:00
    • 4.8 Bias_Variance Treadoff 00:00:00
    • 4.9 Holdout Data Validation 00:00:00
    • 4.10 Ten Fold CV 00:00:00
    • 4.11 Kfold CV 00:00:00
    • 4.12 Conclusion 00:00:00
    • 00:00:00
    • Handout – Neural Networks in R 00:00:00
    • 5.1 Introduction and LogReg Recap FREE 00:00:00
    • 5.2 Decision Boundary FREE 00:00:00
    • 5.3 Non Linear Decision Boundary NN 00:00:00
    • 5.4 Non Linear Decision Boundary and Solution 00:00:00
    • 5.5 Neural Network Intution 00:00:00
    • 5.6 Neural Networks Algorithm 00:00:00
    • 5.7 Neural Network Algorithm Demo 00:00:00
    • 5.8 Building a Neural Network 00:00:00
    • 5.9 Local vs Global Min 00:00:00
    • 5.10 Lab Digit Recognizer 00:00:00
    • 5.10.1 Digit Recognizer Second Attempt Part 1 00:00:00
    • 5.10.2 Digit Recognizer Second Attempt Part 2 00:00:00
    • 5.11 Conclusion 00:00:00
    • Neural Networks Quiz Unlimited
    • Handout – Support Vector Machine 00:00:00
    • 6.1 Introduction To SVM FREE 00:00:00
    • 6.2 The Classifier and Decision Boundary FREE 00:00:00
    • 6.3 SVM – The Large Margin Classifier 00:00:00
    • 6.4 The SVM Algorithms and Results 00:00:00
    • 6.5 SVM on R 00:00:00
    • 6.6 Non Linear Boundary 00:00:00
    • 6.7 Kernal Trick 00:00:00
    • 6.8 Kernal Trick on R 00:00:00
    • 6.9 Soft Margin and Validataion 00:00:00
    • 6.10 SVM Advantages, Disadvantages and Applications 00:00:00
    • 6.11 Lab Digit recognize 00:00:00
    • 6.12 SVM Conclusion 00:00:00
    • SVM Quiz Unlimited
    • Handout – Random Forest and Boosting 00:00:00
    • 7.1 Introduction to Bagging RF Boosting FREE 00:00:00
    • 7.2 Wisdom of Crowd FREE 00:00:00
    • 7.3 Ensemble Learning 00:00:00
    • 7.4 Ensemble Models 00:00:00
    • 7.5 Bagging 00:00:00
    • 7.6 Bagging Models LAB 00:00:00
    • 7.7 Random Forest 00:00:00
    • 7.8 Random Forest LAB 00:00:00
    • 7.9 Boosting 00:00:00
    • 7.10 Boosting Illustration 00:00:00
    • 7.11 Boosting LAB 00:00:00
    • 7.12 Conclusion 00:00:00
    • RF and Boosting Quiz Unlimited
    • Handout – Cluster Analysis 00:00:00
    • 8.1 Introduction to Clustering via Segmentation FREE 00:00:00
    • 8.2 Types of Clusters FREE 00:00:00
    • 8.3 Similarities and Dissimilarity 00:00:00
    • 8.4 Calculating the Distance 00:00:00
    • 8.5 Calculating Distance in R 00:00:00
    • 8.6 Clustering Algorithms – Kmeans 00:00:00
    • 8.7 Kmeans Clustring on R 00:00:00
    • 8.8 More on Kmeans 00:00:00
    • 8.9 Data Stanndardisation and Non-numeric Data 00:00:00
    • 8.10 Conclusion 00:00:00
    • Cluster Analysis Unlimited