Course Learning Path
- 1. Introduction to R
- 2. Data Preparation in R
- 3. Statistics essentials
- 4. R Environment Setup and Essentials
- 5. Supervised Learning
- 6. Unsupervised Learning
- 7. Tree-Based Models in R
- R Essentials – Vectors, Matrices, Factors, Dataframes, Lists, Functions, Apply Family
- Visualization – ggplot2, Visualization using Graphics, Histograms, Line/Bar/Box Plots
- Data Wrangling – Filter, Mutate and Arrange data using gapminder and dplyr packages
- Grouping and Summarizing – summarize and group_by
- Loops, Control Flow
- Importing Data in R – Flat Files, Excel, Databases, Web or statistical softwares (SAS)
- Tidying, Combining and Cleaning Data for Analysis
- Hypothesis Testing
- Parametric Test
- Non-Parametric Test
- Data Sampling
- Confidence Intervals and Significance Levels
- Installing Packages
- Working with environments
- Classification – KNN, Naïve Bayes, SVM
- Regression – Linear, Logistic, GLMs, Multiple
- Model Evaluation – RMSE, AUC, ROC, MSE, R-square, Adjusted R-square
- Model Tuning
- Data Preprocessing – Missing Data Handling, Data Imputation, Centering and Scaling
- Clustering – K-means, Hierarchical Clustering and t-SNE
- Dimension Reduction – Principal Component Analysis
- Classification and Regression Trees (CART)
- Decision Trees
- Random Forests
- Boosting
- Model Tuning
- Bias-Variance Tradeoff – Understanding the concept of overfitting and under-fitting
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