Cluster Analysis : Unsupervised Machine Learning In Python

Cluster Analysis : Unsupervised Machine Learning In Python
Published 7/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

A Quick Way to Learn and Implement Clustering Algorithms for Pattern Recognition in Python. A Course for Beginners.

What you’ll learn
Describe the input and output of a clustering model
Prepare data with feature engineering techniques
Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models
Determine the optimal number of clusters
Requirements
Basic knowledge of Python Programming
Description
Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 3 Machine Learning

Lecture 4 Supervised Learning

Lecture 5 Supervised Learning: Classifications

Lecture 6 Supervised Learning: Regressions

Lecture 7 Unsupervised Learning

Lecture 8 Unsupervised Learning : Clustering

Lecture 9 Installation of Python Platform

Section 2: Building and Evaluating Clustering ML Models

Lecture 10 Important Terminologies

Lecture 11 K-Means Clustering

Lecture 12 Hierarchical Clustering

Lecture 13 Silhouette Score

Lecture 15 Davies-Bouldin Index

Lecture 16 Mean Shift Clustering

Lecture 17 DBSCAN : Density Based Spatial Clustering of Applications with Noise

Lecture 18 OPTICS : Ordering points to identify the clustering structure

Lecture 19 Spectral Clustering