Master Cluster Analysis And Unsupervised Learning [2024]
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Master Cluster Analysis And Unsupervised Learning [2024]
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.10 GB | Duration: 5h 27m
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
Welcome to the course Master Cluster Analysis and Unsupervised Learning!Cluster Analysis and Unsupervised learning are one of the most important and defining tasks within machine learning and data science. Cluster Analysis and Unsupervised learning are one of the main methods for data scientists, analysts, A.I., and machine intelligences to create new insights, information or knowledge from data.This course is a practical and exciting hands-on master class video course about mastering Cluster Analysis and Unsupervised Learning.You will be taught to master some of the most useful and powerful Cluster Analysis and unsupervised learning techniques available...You will learn to:Master Cluster Analysis and Unsupervised Learning both in theory and practiceMaster simple and advanced Cluster Analysis modelsUse K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Principal Component Analysis, and more.Evaluate Cluster Analysis models using many different toolsLearn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated SimulationsGain Understanding of concepts such as truth, predicted truth or model-based conditional truthUse effective advanced graphical tools to judge models' performanceUse the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and PythonCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resourcesOption: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life.And much more.This course is an excellent way to learn to master Cluster Analysis and Unsupervised Learning!Cluster Analysis and Unsupervised Learning are considered exploratory types of data analysis and are useful for discovering new information and knowledge. Unsupervised Learning and Cluster Analysis are often viewed as one of the few ways for artificial intelligences and machine intelligences to create new knowledge or data information without human assistance or supervision, so-called supervised learning.This course provides you with the option to use Cloud Computing with the Anaconda Cloud Notebook and to learn to use Cloud Computing resources, or you may use any Python capable environment of your choice.This course is designed for everyone who wants tolearn to Master Cluster Analysis and Unsupervised LearningRequirements:Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionSome Python skill is necessary and some experience with the Pandas library is recommendedThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedThis course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Cluster Analysis, and Unsupervised Learning.Enroll now to receive 5+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
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
https://rapidgator.net/file/5c2c6e36d70891d7d2f6ff091cb8bbc4/Master_Cluster_Analysis_and_Unsupervised_Learning_2024.part1.rar.html
https://rapidgator.net/file/0530492d0db96f25279742ea36f4d51f/Master_Cluster_Analysis_and_Unsupervised_Learning_2024.part2.rar.html
https://rapidgator.net/file/0274092e435d21c925f19894ff75194e/Master_Cluster_Analysis_and_Unsupervised_Learning_2024.part3.rar.html
https://rapidgator.net/file/2f840b77536ec91fbedf2e1d6c68baa4/Master_Cluster_Analysis_and_Unsupervised_Learning_2024.part4.rar.html
