Ultimate ML Bootcamp #7: Unsupervised Learning

Name: Ultimate ML Bootcamp #7: Unsupervised Learning
Rating: 4.7 (9 reviews)

Master the Fundamentals of Unsupervised Learning

Created byMiuul Data Science & Deep Learning

Last updated 9/2024

English

English [Auto],

What you'll learn

Understand and implement K-Means clustering to uncover patterns in unlabeled data.
Apply Hierarchical Clustering methods to group similar data points based on their characteristics.
Utilize Principal Component Analysis (PCA) to reduce data dimensionality while preserving key features.
Conduct Principal Component Regression (PCR) for predictive modeling in high-dimensional data spaces.

Course content

1 section • 13 lectures • 1h 30m total length

Course Materials0:03
Introduction to Unsupervised Learning1:03
Explore unsupervised learning, where there is no dependent variable, and contrast it with supervised learning. Learn three methods—k means method, hierarchical cluster analysis, and principal component analysis.
What is K-Means?10:12
Explore how the k-means unsupervised learning method clusters observations by proximity using distance metrics, iteratively updating random centers to minimize the sum of squared errors within clusters.
Application I: K-Means9:40
Apply k-means clustering to the USA arrests dataset, using min-max scaling, standardization, and optional PCA, then compare cluster centers and inertia to determine the optimal number of clusters.
Application II: K-Means7:51
Determine the optimal number of clusters in k-means by the elbow method, using SSD and distortion scores, testing k from 1 to 30, and balancing with business knowledge.
Application III: K-Means6:08
Fit the final k-means model with elbow-determined five clusters, assign labels to the data frame, and export the cluster assignments to clusters.csv.
What is Hierarchical Clustering?3:49
Explore hierarchical clustering methods, using agglomerative and divisive approaches to form clusters based on similarities, from leaves up or from one cluster down, with lines defining levels.
Application I: Hierarchical Clustering3:56
Apply hierarchical clustering to the USA arrest data by standardizing variables and using agglomerative linkage with a dendrogram to determine the optimal number of clusters.
Application II: Hierarchical Clustering6:16
Explore determining the number of clusters from dendrograms by drawing threshold lines and comparing hierarchical clustering with K-means, then assign cluster labels with agglomerative methods.
What is PCA?5:09
Apply principal component analysis to reduce multivariate data to fewer uncorrelated components with minimal information loss. Rely on eigenvalues to rank variance and guide dimension reduction, visualization, and noise reduction.
Application: PCA7:17
Apply principal component analysis to the hitter's numerical data to reduce 16 variables to 2–3 components and assess success with explained variance ratios and the elbow method.
PCA Visualization15:27
Learn to visualize high-dimensional data with PCA, reducing to two components, and graphically separate classes using breast cancer and iris datasets through a general PCA visualization function.
What is PCR?13:18
Use principal component regression to reduce 16 numeric variables to three uncorrelated components. Then build and evaluate a regression model with cross-validation, addressing multicollinearity.

Requirements

Basic understanding of machine learning concepts and familiarity with Python programming.

Description

Welcome to the seventh chapter of Miuul's Ultimate ML Bootcamp—a comprehensive series designed to elevate your expertise in machine learning with a focus on unsupervised learning techniques. In this chapter, "Unsupervised Learning," we will dive into the world of machine learning where the data lacks predefined labels, uncovering the hidden structures and patterns that emerge from raw data.

This chapter begins with an Introduction to Unsupervised Learning, setting the stage by exploring the key concepts and importance of this approach in the context of data analysis. You will then move on to one of the most widely used clustering techniques, K-Means, starting with a theoretical foundation and progressing through multiple practical applications to illustrate its effectiveness in real-world scenarios.

Next, we'll shift our focus to Hierarchical Clustering, another powerful method for discovering structure within data. You will learn the mechanics of this technique and apply it through hands-on sessions that demonstrate its utility across various datasets.

As we continue, we'll introduce you to Principal Component Analysis (PCA), a dimensionality reduction technique that simplifies data while preserving its essential characteristics. The chapter will cover both the theory and practical applications of PCA, along with visualization techniques to help interpret and understand the transformed data.

Finally, the chapter concludes with Principal Component Regression (PCR), combining the strengths of PCA and regression analysis to improve predictive modeling in high-dimensional spaces.

Throughout this chapter, you will gain a deep understanding of the principles and practicalities of unsupervised learning methods. You will learn not only how to implement these techniques but also how to interpret their results to make informed decisions. By the end, you will be equipped with a solid foundation in unsupervised learning, enabling you to uncover patterns and insights from complex datasets with confidence.

We are excited to accompany you on this journey into the fascinating domain of unsupervised learning, where you will learn to find order in chaos and extract meaningful insights from unlabeled data. Let's dive in and unlock new dimensions of your analytical capabilities!

Who this course is for:

Aspiring data scientists, analysts, and machine learning enthusiasts looking to deepen their knowledge of unsupervised learning techniques.