Udemy – Master Simplified Unsupervised Machine Learning End to End ™ 2024-11

Published on: 2024-12-28 12:30:45

Categories: 28

Description

Master Simplified Unsupervised Machine Learning End to End™ Course. This course is a comprehensive program designed to provide a deep understanding of unsupervised learning techniques, algorithms, and applications in data science and machine learning. This course demystifies unsupervised learning, covering everything from fundamental concepts to advanced clustering, dimensionality reduction, and association rule extraction. Learners gain practical skills in pattern recognition, data segmentation, and uncovering hidden structures in unlabeled data, equipping them with powerful tools for real-world applications across industries. With this course, learners will be fully prepared to apply unsupervised techniques to uncover insights, drive decision-making, and unleash the full potential of data.

What you will learn in this course:

Understand the basic principles and techniques of unsupervised learning.
Mastery of deviation detection methods to identify outliers in data sets.
Deep understanding and application of K-Means clustering in unsupervised learning.
Iterate and optimize the K-Means clustering algorithm for better results.
Practical applications of the K-Means clustering algorithm in real-world scenarios.
Mastering hierarchical clustering and understanding its benefits in unsupervised learning.
Visualizing hierarchical clustering using dendrograms for better interpretation.
Application of hierarchical clustering to solve complex clustering problems.
Learning the DBSCAN algorithm and its effectiveness in density-based clustering.
Exploring the benefits of DBSCAN in managing complex clustering patterns.
Introducing Principal Component Analysis (PCA) for dimensionality reduction.
Selecting optimal components in PCA for effective dimensionality reduction.
Application of Principal Component Analysis (PCA) to real-world data for dimensionality reduction.
Understanding Linear Discriminant Analysis (LDA) for Unsupervised Learning Tasks.
Comparison of PCA vs. LDA in terms of dimensionality reduction and classification.
Application of Linear Discriminant Analysis (LDA) for Classification Optimization in Unsupervised Learning.
Master t-SNE for advanced dimensionality reduction and visualization of high-dimensional data.
Understand how t-SNE works and use it effectively for data visualization.
Practical applications of t-SNE in dimensionality reduction and visualization of complex datasets.
Investigating various evaluation criteria for unsupervised learning models for clustering algorithms.
Understanding and applying dimensionality reduction evaluation criteria for model evaluation.
Learning hyperparameter tuning techniques in unsupervised learning models.
Using Bayesian optimization to improve the performance of unsupervised learning models.
Introducing association rule mining for market basket analysis and more.
Understanding confidence and support in extracting association rules for actionable insights.
Learning Apriori algorithm for efficient extraction of association rules and market portfolio analysis.
Step-by-step application of the Apriori algorithm to discover valuable patterns in data.

This course is suitable for people who:

Anyone who wants to learn the skills of the future and become a data scientist, senior data scientist, AI scientist, AI engineer, AI researcher, and AI specialist.