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Clustering and Dimensionality Reduction

Introduction

Introduction (0:33)
Unsupervised machine learning (4:58)
Course contents (4:40)
Learning tips (4:06)
Jupyter notebook overview (4:45)
How did you hear about us?
Refer a friend program

Python basics

Chapter agenda (1:14)
---- Part 1 - basic python data types ---- (1:15)
Numerical data types (4:59)
Boolean data type (8:48)
String data type (4:41)
Python lists - part 1 (9:16)
Python lists - part 2 (11:42)
Sets and tuples (6:12)
Dictionaries and "None" (6:29)
Truthiness (3:34)
---- Part 2 - basic python functionalities ---- (0:56)
Copying in python (shallow & deep copying) (8:24)
Unpacking iterable data types (4:30)
Python functions, *args and **kwargs (11:28)
Python functions - demo (9:22)
Lambda functions, scopes and decorators (11:31)
Python classes (8:48)
Python classes - demo (10:18)
While loops and loop control statements (5:04)
Comprehensions in python (12:27)
Chapter summary (0:42)
How are we doing? (0:26)

Python data science libraries

Chapter agenda (0:49)
---- Part 1 - Numpy ---- (3:34)
Indexing & slicing in numpy (12:22)
Indexing & slicing in numpy - demo (10:52)
Operations on single numpy arrays (9:53)
Operations on single numpy arrays - demo (4:17)
Operations between numpy arrays & broadcasting (7:12)
Operations between numpy arrays & broadcasting - demo (2:25)
Merging numpy arrays (4:27)
Data types in numpy (3:10)
Matrix operations in numpy (1:49)
---- Part 2 - pandas ---- (4:55)
Pandas indexing and slicing (9:09)
Creating data frames (6:28)
Pandas indexing and slicing - demo (8:54)
Operations on single data frames/series (12:51)
Operations on single data frames/series - demo (4:07)
Operations between data frames/series (9:53)
Operations between data frames/series - demo (5:15)
Other useful pandas functionalities (8:25)
Pandas data types (4:14)
Pandas data types - demo (2:18)
Pandas group by statement (4:19)
Pandas group by statement - demo (4:10)
---- Part 3 - Data visualisations ---- (7:18)
Matplotlib basics (6:53)
Seaborn basics (4:40)
Chapter summary (0:48)
How are we doing? (0:26)

K-means clustering - part 1

Chapter agenda (2:32)
K-means clustering algorithm (11:55)
Avoiding suboptimal solutions (8:06)
Demo: Implementing k-means clustering algorithm from scratch - part 1 (13:23)
Demo: Implementing k-means clustering algorithm from scratch - part 2 (8:13)
K-means in sklearn (8:00)
Data preprocessing for K-means (13:09)
Adjusted rand index (16:09)
Demo: Data preprocessing, k-means & adjusted rand index (9:16)
Inferring number of clusters with inertia knee method (12:01)
Silhouette scores (inferring number of clusters & analyzing cluster quality) (14:31)
Demo: inertia knee method & silhouette scores - part 1 (13:21)
Demo: inertia knee method & silhouette scores - part 2 (8:05)
Chapter summary (1:55)
How are we doing? (0:26)

Principal component analysis (PCA)

Chapter agenda (1:32)
Feature cordinate systems (3:45)
PCA and feature corinate systems (16:13)
Intuition behind Principal component analysis (12:45)
PCA as a linear transformation of the data - introduction (1:25)
Linear transformations (11:59)
Eigenvectors and eigenvalues (6:33)
Change of basis (13:04)
Variance and covariance (12:30)
PCA from eigendecomposition perspective (15:20)
Principal component analysis for dimensionality reduction (21:19)
Demo: Performing PCA by using eigendecomposition (11:32)
Principal component analysis in sklearn (12:34)
Demo: PCA in sklearn (artificial data) (4:54)
Demo: PCA in sklearn (real data) (10:04)
Guidelines for choosing number of principal component (6:30)
Demo : Choosing number of principal components (9:43)
Chapter summary (1:10)
How are we doing? (0:26)

Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP)

Chapter agenda (1:19)
Graph theory basics (9:05)
UMAP introduction (11:00)
Fuzzy set basics (4:59)
Gradient descent & stochastic gradient descent (17:48)
Sparse matrices with SciPy (12:31)
UMAP theory - part 1 (26:39)
Demo: Implementing UMAP from scratch - part 1 (20:08)
UMAP theory - part 2 (27:50)
Speeding up python code with numba (4:29)
Demo: Implementing UMAP from scratch - part 2 (14:54)
UMAP python package (umap-learn) (6:10)
Demo: Running UMAP with umap-learn python package (5:24)
Tuning UMAP parameters (7:40)
Demo: Tuning UMAP parameters (5:53)
UMAP caveats (12:52)
Demo: UMAP caveats (3:59)
Chapter summary (1:08)

K-means clustering - part 2

Chapter agenda (3:10)
Yellowbrick python library (6:19)
Characterizing clusters using data visualizations (3:15)
Demo: K-means, yellowbrick and cluster characterization (16:55)
Handling and encoding categorical features (10:22)
Encoding categorical data in python (12:04)
Measuring distance in categorical data (4:20)
Distance measures & distance metrics (4:02)
Calculating distance with SciPy & choosing distance measures in other algorithms (9:05)
Demo: Calculating distances with SciPy and sklearn (applied to categorical data) (5:28)
K-modes clustering algorithm (10:24)
K-modes python package (5:10)
Demo: Clustering categorical data using the K-means algorithm (8:41)
Demo: Clustering categorical data using the K-modes algorithm (14:05)
Mixed data & gower distance (10:23)
K-prototypes clustering algorithm (6:56)
K-prototypes python package (3:47)
Clustering customers demo prerequisites (12:36)
Demo: Clustering customers (mixed data) (31:46)
K-means algorithm pros & cons (5:42)
Demo: k-means algorithm limitations (3:00)
Chapter summary (1:21)

Case study - clustering cells based on RNA data

Understanding the data - central dogma of molecular biology (8:21)
Case study intro (1:46)
Understanding the data - single cell RNA sequencing (4:22)
Analyzing the data - removing low quality cells (13:06)
Analyzing the data - normalization, gene selection, PCA, UMAP and clustering (15:21)
Demo prerequisite - statistical testing basics (14:45)
Demo: analyzing the data - part 1 (13:30)
Demo: analyzing the data - part 2 (16:15)
Case study summary (2:11)

Agglomerative hierarchical clustering

Chapter agenda (2:24)
Hierarchical & agglomerative clustering introduction (6:47)
Dendrogram linkages & constructing dendrograms (16:16)
Cophenetic distance & cophenetic correlation (3:28)
Constructing dendrograms with SciPy (5:50)
Demo: Constructing dendrograms with SciPy (8:31)
Approaches for extracting clusters from dendrograms (12:36)
Agglomerative clustering with SciPy and sklearn (7:55)
Demo: Agglomerative clustering with SciPy & dendrogram manipulation (18:01)
Demo: Agglomerative clustering with sklearn (6:20)
Agglomerative clustering general guidelines (8:53)
Demo: Clustering cars (numerical data) (13:44)
Demo: Clustering animals (categorical data) (6:21)
Demo: Clustering cars (mixed data) (13:01)
Chapter summary (1:29)

Density based clustering

Chapter agenda (2:26)
Density based clustering - introduction (3:43)
DBSCAN clustering algorithm (15:31)
Nearest neighbors basics (13:32)
Nearest neighbors in sklearn (5:25)
Demo: implementing DBSCAN from scratch (10:21)
DBSCAN in sklearn (2:54)
Tuning DBSCAN parameters (10:40)
Demo: Tuning DBSCAN parameters (3:56)
Density based clustering validation (DBCV) - part 1 (11:30)
Density based clustering validation (DBCV) - part 2 (15:17)
Demo: Implementing DBCV from scratch - part 1 (13:40)
Demo: Implementing DBCV from scratch - part 2 + DBCV python function (5:47)
DBSCAN general guideliness (4:49)
Demo: Clustering digits (mnist784) with DBSCAN (10:59)
Demo: Clustering animals with DBSCAN (categorical data) (5:03)
HDBSCAN clustering algorithm - part 1 (6:22)
HDBSCAN clustering algorithm - part 2 (14:11)
HDBSCAN clustering algorithm - part 3 (3:15)
HDBSCAN python library (hdbscan) (7:32)
Demo: Implementing HDBSCAN (partial implementation) (14:22)
HDBSCAN general guideliness (7:08)
Demo: Clustering iris and digits (mnist784) with HDBSCAN (7:37)
Demo: Clustering animals with HDBSCAN (categorical data) (2:29)
Robust scaler (demo prerequisite) (4:12)
Demo: Clustering phones with HDBSCAN (mixed data) (7:34)
Case study: Geospatial clustering with DBSCAN and HDBSCAN - introduction (6:40)
Case study: Geospatial clustering with DBSCAN and HDBSCAN (14:33)
Chapter summary (1:48)

Graph based clustering

Chapter agenda (2:10)
Graphs, graph layouts and graph communities (8:30)
Igraph python library (1:16)
Demo: Igraph library capabilities (11:58)
Modularity in graph community structures (6:07)
Louvain clustering algorithm (16:18)
Louvain clustering - resolution parameter (4:26)
Demo: Implementing Louvain from scratch (19:49)
Analyzing community structure quality (8:49)
Igraph - other useful functionalities (7:22)
Demo: Community quality metrics (12:15)
Case study: Clustering actors (15:46)
Using graph clustering with numerical/categorical/mixed data (KNN graph) (10:13)
Shared neighbors graph (SNN graph) (4:11)
Creating KNN (k nearest neighbors) graphs with sklearn (2:30)
Graph clustering guidelines (5:22)
Louvain algorithm pros & cons (3:01)
Demo: Clustering digits (mnist784) with Louvain (15:31)
Demo: Clustering animals with Louvain (categorical data) (3:55)
Chapter summary (1:09)

Wrap-Up

Chapter agenda (0:40)
Single cell RNA case study - part 2 (11:03)
Clustering and dimensionality reduction - summary (11:18)
Course end (0:30)

Operations between numpy arrays & broadcasting

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