You can download all data and code for all sections at once.
All Code and Data (1.0 GB)
You can download data and code for all individual sections of the Udemy course.
Section 1: Introduction
| Lecture Title | Filename |
|---|---|
| Teaser | Lab |
Section 2: R Refresher
| Lecture Title | Filename |
|---|---|
| Rmarkdown | Lab |
| Data Manipulation | Lab |
| Data Reshaping | Lab |
| Package Preparation | Lab |
Section 3: Regression
| Lecture Title | Filename |
|---|---|
| Univariate Regression | Lab |
| Exercise/Solution | |
| Polynomial Regression | Lab |
| Multivariate Regression | Lab |
| Exercise / Solution |
Section 5: Model Preparation and Evaluation
| Lecture Title | Filename |
|---|---|
| Train/Validation/Test Split | Lab |
| Resampling Techniques | Lab |
Section 6: Regularization
| Lecture Title | Filename |
|---|---|
| Regularization | Lab |
Section 8: Classification
| Lecture Title | Filename |
|---|---|
| ROC Curve |  101 Slides
Lab |
Section 9: Decision Trees
| Lecture Title | Filename |
|---|---|
| Decision Trees | Lab |
| Exercise/Solution |
Section 10: Random Forests
| Lecture Title | Filename |
|---|---|
| Random Forests | Lab |
| Exercise/Solution |
Section 11: Logistic Regression
| Lecture Title | Filename |
|---|---|
| Logistic Regression | Lab |
| Exercise/Solution |
Section 12: Support Vector Machines
| Lecture Title | Filename |
|---|---|
| Support Vector Machines | Lab |
| Exercise/Solution |
Section 13: Ensemble Models
| Lecture Title | Filename |
|---|---|
| Ensemble Models | Lab |
Section 15: Apriori
| Lecture Title | Filename |
|---|---|
| Apriori | Lab |
| Exercise/Solution |
Section 17: kmeans
| Lecture Title | Filename |
|---|---|
| kmeans | Lab |
| Exercise/Solution |
Section 18: Hierarchical Clustering
| Lecture Title | Filename |
|---|---|
| Hierarchical Clustering | Lab |
Section 19: Dbscan
| Lecture Title | Filename |
|---|---|
| dbscan | Lab |
Section 21: Principal Component Analysis
| Lecture Title | Filename |
|---|---|
| PCA | Lab |
| Exercise/Solution |
Section 22: t-SNE
| Lecture Title | Filename |
|---|---|
| t-SNE | Lab |
Section 23: Factor Analysis
| Lecture Title | Filename |
|---|---|
| Factor Analysis | Lab |
| Exercise/Solution |
Section 24: Upper Confidence Bound
| Lecture Title | Filename |
|---|---|
| UCB | Lab |
Section 25: Deep Learning Introduction
| Lecture Title | Filename |
|---|---|
| Modeling | 101 Slides |
| Performance | 101 Slides |
| From Perceptron to Neural Network | 101 Slides |
| Layer Types | 101 Slides |
| Activation Functions | 101 Slides |
| Loss Functions | 101 Slides |
| Optimizers | 101 Slides |
| Frameworks | 101 Slides |
Section 26: Deep Learning Regression
| Lecture Title | Filename |
|---|---|
| Multi-Target Regression | Lab |
Section 27: Deep Learning Classification
| Lecture Title | Filename |
|---|---|
| Binary Classification | Lab |
| Multi-Label Classification | Lab (638 MB) |
Section 28: Convolutional Neural Networks
| Lecture Title | Filename |
|---|---|
| CNN for Classification | Lab (140 MB) |
| Exercise/Solution (196 MB) | |
| Semantic Segmentation | Lab(400 MB) |
Section 29: Autoencoders
| Lecture Title | Filename |
|---|---|
| Autoencoders | Lab |
Section 30: Pretrained Models and Transfer Learning
| Lecture Title | Filename |
|---|---|
| Pretrained Models | Lab |
Section 31: Recurrent Neural Networks
| Lecture Title | Filename |
|---|---|
| RNN, especially LSTM | Lab/Exercise/Solution |
Section 32: Bonus
| Lecture Title | Filename |
|---|---|
| Model Interpretation | Lab |