Course curriculum

  • 1

    Machine Learning - Unsupervised Learning Course

  • 2

    Practice Exercises Notebook - GitHub

    • Practice Exercises Notebooks

  • 3

    Group Learning & QA Community

    • Group Learning and Q/A Community

    • Ecosia Search Engine

  • 4

    Overview of Machine Learning

    • 2.1.What is Machine Learning?

    • 2.2.Phases of Machine Learning Model

    • 2.3.Different ways in which Machine Learn

    • 2.4.Types of Machine Learning

    • 2.5.Machine Learning Full View Recap

  • 5

    Introduction to Unsupervised Learning

    • 3.1.Introduction_to_Unsupervised_Learning

    • 3.3.Type_of_UL_algorithms

    • 3.2.Unsupervised_learning_process

  • 6

    What is Clustering?

    • 4.1.What_is_clustering

    • 4.2.How_clustering_different_from_classification

    • 4.3.Clustering_example

    • 4.4.What_clustering_do_exactly

    • 4.5.What_are_good_clusters

    • 4.6.Types_of_clustering

  • 7

    Similarity & Dissimilarity Measures

    • 5.1.Similarity_n_Dissimilarity

    • 5.2.Similarity_measure

    • 5.3.Similarity_measure_algorithms

    • 5.4.Two_groups_of_Similarity_measure

    • 5.5.Cosine_similarity

    • 5.6.Jaccard_similarity

    • 5.7.Euclidean_distance

    • 5.8.Manhattan_distance

    • 5.9.Minkowski_distance

    • 5.10.Distance_computation_assignment

    • 5.11.Reading_assignment

  • 8

    Types of Clustering Algorithms

    • 6.1.Types_of_clustering_algorithms

    • 6.2.Clustering_algorithm_connectivitiy_based

    • 6.3.Clustering_algorithm_centroid_based

    • 6.4.Clustering_algorithm_density_based

    • 6.5.Drawback_of_density_and_boundary_based_approaches

    • 6.6.Clustering_algorithm_distribution_based

    • 6.7.Advantages_disadvantages_of_clustering_algorithms

    • 6.8.Types_of_clustering_algorithms_extension

    • 6.9.Reading_assignment

  • 9

    Hierarchical Clustering Algorithm

    • 7.1.H-clustering_algorithm

    • 7.2.H-clustering_algorithm_Introduction

    • 7.3.H-clustering_algorithm_steps

    • 7.4.H-clustering_merge_clusters

    • 7.5.H-clustering_single_linkage

    • 7.6.H-clustering_properties

    • 7.7.H-clustering_R

    • 7.8.H-clustering_R_implementation_1

    • 7.9.H-clustering_R_implementation_2

    • Class Example H-clustering Algorithm

    • 7.10.Reading_Assignemnt_&_Exercise

    • Reading & Practice Examples

    • Divisive Hierarchical Clustering - Practical Exercise

  • 10

    K-Means Clustering Algorithm

    • 8.1.Kmean_clustering_module

    • 8.2.Kmean_clustering_introduction

    • 8.3.Kmean_clustering_algorithm

    • 8.4.Kmean_clustering_algorithm_visual

    • 8.5.Kmean_clustering_problem

    • 8.6.Kmean_clustering_R

    • 8.7.Kmean_clustering_R_visualisation

    • 8.8.Kmean_clustering_Optimal_cluster

    • K-means clustering class example

    • 8.9.Kmean_clustering_advantanges_disadvantages

    • 8.10.Kmean_clustering_use_cases_practice

    • Use Cases Practice Exercises

    • Publish Kaggle Notebook - Computer Specification Dataset

  • 11

    DBSCAN - Density-based Clustering

    • 9.1.DBSCAN_clustering_algorithm

    • DBSCAN Clustering - Exercise

  • 12

    Cluster Evaluation Metrics

    • 10.1.Clustering_Evaluation_Matric

  • 13

    Cluster Visualisation

    • 11.1.Cluster_visualisation-kmeans

    • 11.2.Cluster_visualisation_hclust

    • 11.3.Cluster_visualisation_factoextra_lib

  • 14

    Kaggle Competitions

    • 12. Kaggle_datasets

  • 15

    Course Conclusion and CV Guide

    • 13.Course_Conclusion

  • 16

    Solution Link Submission

    • Solution Link Submission

  • 17

    Course Slides and Material

    • Unsupervised Learning - Lecture Slides