Master Cluster Analysis And Unsupervised Learning [2024]

Master Cluster Analysis And Unsupervised Learning [2024]
Published 10/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Learn to Master Cluster Analysis and Unsupervised Learning for Data Science, Data Analysis, and Machine Learning [2024]

What you’ll learn

Master Cluster Analysis and Unsupervised Learning both in theory and practice

Master simple and advanced Cluster Analysis models

Use K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Principal Component Analysis, and more.

Evaluate Cluster Analysis models using many different tools

Learn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated Simulations
Gain Understanding of concepts such as truth, predicted truth or model-based conditional truth

Use effective advanced graphical tools to judge models’ performance

Use the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and Python

Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Requirements

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection

Some Python skill is necessary and some experience with the Pandas library is recommended

The course only uses costless software

Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included
Description

Overview

Section 1: Introduction

Lecture 1 Overview and Introduction

Lecture 2 Setup of the Anaconda Cloud Notebook

Lecture 3 Download and installation of the Anaconda Distribution (optional)

Lecture 4 The Conda Package Management System (optional)

Section 2: Master Cluster Analysis and Unsupervised Learning

Lecture 5 Overview

Lecture 6 K-Means Cluster Analysis

Lecture 7 Auto-updated K-Means Cluster Analysis, introduction and simulation
Lecture 8 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Lecture 9 Four Hierarchical Clustering algorithms

Lecture 10 Principal Component Analysis (PCA)

Everyone who wants to learn to Master Cluster Analysis and Unsupervised Learning