
Overview and introduction and to the video course
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address
Note: Anaconda often updates its resources and user interface plus utilizes anti-drone technology. This may cause minor deviations from graphics and procedures in the video
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
This video describes the Conda Package Management System
Conda requires internet access
Video is optional
Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures
This video provides an overview of the Master Cluster Analysis and Unsupervised Learning section, and some theory on Cluster Analysis and Unsupervised Learning
Learn to use K-Means Cluster Analysis in a deep, practical and hands-on fashion. Learn to use practical and useful knee/elbow inertia plots and silhouette score plots. Use visualization tools to compare K-Means Cluster Analysis with subject matter expert classifications on a dataset
Extend your knowledge about K-means Cluster Analysis to Auto-updated / prototyped simulations. Learn some about the most important and defining tasks within machine learning and data science. Gain understanding about concepts such as truth, predicted truth, and model-based conditional truth.
Learn about data quality, model quality, practical data analysis, simulations and some new ways to study and graph Cluster Analysis models
Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An exploratory analysis searching for data structures in the sized California Housing Dataset
Hierarchical Cluster Models. The Ward, Single, Average, and Complete linkage models. Dendrogram graphs for small-sized datasets. Exploratory analysis searching for structures in the California Housing Dataset
Learn to use Principal Component Analysis in a practical and hands-on fashion with some theory. Learn to use Principal Components as a technique for data transformations and dimensionality reduction
Learn to make Scree plots, heatmaps, and Indices plots plus learn to use these plots for component selections and dimensionality reduction. Learn to create uncorrelated Principal Component Loading to augment supervised learning models
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 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
Option: 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 to
learn to Master Cluster Analysis and Unsupervised Learning
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
This 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!