Digiroof

1 - Machine Learning, difference between clustering & classification, supervised & unsupervised

2 - clustering benefits, meaning of unlabeled data

3 - understand clustering using some examples

4 - going through the course content, target audience

6 - Different types of data - ordinal & nominal data, Scaling the data

7 - create random dataset in R, some datasets used in the course

8 - using small subset to understand euclidean distance measure

9 - manually calculating euclidean distance

10 - randomness in the clustering process

11 - Make two clusters using randomly chosen cluster centroids

12- make a scatterplot in R using the identified clusters

13- Introduction to K means clustering, k means function in R, Understanding the output

14- imdb dataset, Output of k means function, Understanding R codes

15- recap k means clustering process, Understand R codes

16 - Making clusters using automobile data, Understand R codes, make scatterplot in R

17 - Introduction to manhattan distance, use automobile dataset to calculate manhattan distance

18- formula manhattan distance, visualize the clusters in R

Chapter -2 (Exercise Files)

19 - Understand Partitioning Around Medoids Clustering

20 - Introduction to Hierarchical Clustering

21 - Introduction to Single Linkage form of Hierarchical clustering

22 - Complete linkage form of hierarchical clustering

23 - Average linkage form of hierarchical clustering (first method)

24- Average linkage form of hierarchical clustering (second method)

25 - hclust function in R, Represent clusters using ggplot function

26 - Ward method of Hierarchical clustering, Difference between ward.D and ward.D2

Chapter 3 (Exercise Files)

27 - Cluster Binary data, Simple Matching, Jaccard & Dice coefficient

28 - Convert single Nominal column to multiple Binary column

29 - Convert single Nominal column to multiple Binary column (part 2), Clustering process of converted binary columns

30 - Clustering process of Mixed data

31 - Introduction to Kmodes clustering

32 - Kmodes Clustering, Simple matching dissimilarity

33 - Kmodes clustering- Understanding the process

Chapter - 4 (Exercise Files)

34 - Introduction to Density based clustering

36 - Replace Missing data to improve clustering outcome

Chapter 5 (Exercise Files)

37 - What would be the right number of clusters, Elbow method

38 - Example to understand elbow method, nbclust function in R for determining adequate number of clusters

39 - Introduction to Silhouette method, Using silhouette method in R, visualize the identified clusters

40 - Example to understand Silhouette method

41 - Daisy function to cluster mixed data, Gower coefficient, Some Examples

Chapter 6 (Exercise Files)

Goodbye