Machine Learning with R | Statinfer

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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 Curriculum

00:00:00

Session 1 - Regression Analysis
	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
Session 2 - Logistic Regression
	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
Session 3 - Decision Tree
	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
Session 4 - Model Selection and Cross Validation
	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
Session 5 - Neural Network
	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
Session 6 - Support Vector Machine - SVM
	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
Session 7 - Random Forest and Boosting
	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
Session 8 - Cluster Analysis
	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

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