arrow_back
Back
lock
Getting Started
lock
1 Introduction (1)
lock
2 Motivation for the course Why to use Machine Learning for Predictions (1)
lock
3 What is Machine Leraning and it's main types (1)
lock
4 Overview of Machine Leraning in R (1)
lock
5 Introduction to Section 2 (1)
lock
6 What is R and RStudio (1)
lock
7 How to install R and RStudio in 2020 (1)
lock
8 Lab Install R and RStudio in 2020 (1)
lock
9 Introduction to RStudio Interface (1)
lock
10 Lab Get started with R in RStudio (1)
lock
11 Lab your first prediction model in R (1)
lock
12 Overview of prediction process (1)
lock
13 Components of the prediction models and trade-offs in prediction (1)
lock
14 Introduction to Section 4 (1)
lock
15 Lab Installing Packages and Package Management in R (1)
lock
16 Variables in R and assigning Variables in R (1)
lock
17 Lab Variables in R and assigning Variables in R (1)
lock
18 Overview of data types and data structures in R (1)
lock
19 Lab data types and data structures in R (1)
lock
20 Vectors' operations in R (1)
lock
21 Data types and data structures Factors (1)
lock
22 Dataframes overview (1)
lock
23 Functions in R - overview (1)
lock
24 Lab For Loops in R (1)
lock
25 Read Data into R (1)
lock
26 Overfitting, sample errors in Machine Learning modelling in R (1)
lock
27 Lab Overfitting, sample errors in Machine Learning modelling in R (1)
lock
28 Study design for predictive modelling with Machine Learning (1)
lock
29 Type of Errors and how to measure them (1)
lock
30 Cross Validation in Machine Learning Models (1)
lock
31 Data Selection for Machine Learning models (1)
lock
32 Unsupervised Learning & Clustering theory (1)
lock
33 Hierarchical Clustering Example (1)
lock
34 Hierarchical Clustering Lab (1)
lock
35 Hierarchical Clustering Merging points (1)
lock
36 Heat Maps theory (1)
lock
37 Heat Maps Lab (1)
lock
38 K-Means Clustering Theory (1)
lock
39 Example K-Means Clustering in R Lab (1)
lock
40 K-means clustering Application to email marketing (1)
lock
41 Heatmaps to visualize K-Means Results in R Examplery Lab (1)
lock
42 Selecting the number of clusters for unsupervised Clustering methods (K-Means) (1)
lock
43 How to assess a Clustering Tendency of the dataset (1)
lock
44 Assessing the performance of unsupervised learning (clustering) algorithms (1)
lock
45 Supervised Machine Learning & KNN Overview (1)
lock
46 Lab Supervised classification with K Nearest Neighbours algorithm in R (1)
lock
47 Overview of functionality of Caret R-package (1)
lock
48 Theory Confusion Matrix (1)
lock
49 Lab Calculating Classification Accuray for logistic regression model (1)
lock
50 Lab Receiver operating characteristic (ROC) curve and AUC (1)
lock
51 Regression Short Overview (1)
lock
52 Graphical Analysis for Regression in R and your first linear regression model (1)
lock
53 Correlation in Regression Analysis in R Lab (1)
lock
54 How to know if the model is best fit for your data - An overview (1)
lock
55 Linear Regression Diagnostics (1)
lock
56 AIC and BIC (1)
lock
57 Evaluation of Prediction Model Performance in Supervised Learning Regression (1)
lock
58 Predict with linear regression model & RMSE as in-sample error (1)
lock
59 Prediction model evaluation with data split out-of-sample RMSE (1)
lock
60 Classification and Decision Trees (CART) Theory (1)
lock
61 Lab Decision Trees in R (1)
lock
62 Random Forest Theory (1)
lock
63 Lab Random Forest (1)
lock
64 Lab Machine Learning Models' Comparison & Best Model Selection (1)
lock
65 Final Project Assignment (1)
Preview - Machine Learning in R & Predictive Models Course
Discuss (
0
)
navigate_before
Previous
Next
navigate_next