Cluster Analysis - Unsupervised Machine Learning in Python

Cluster Analysis : Unsupervised Machine Learning in Python, available at $34.99, has an average rating of 4.29, with 19 lectures, 1 quizzes, based on 7 reviews, and has 1029 subscribers.

You will learn about Describe the input and output of a clustering model Prepare data with feature engineering techniques Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models Determine the optimal number of clusters Use a variety of performance metrics such as Silhouette Score, Calinski-Harabasz Index and Davies-Bouldin Index. This course is ideal for individuals who are Beginners starting out to the field of Machine Learning. or Industry professionals and aspiring data scientists. or People who want to know how to write their clustering code. It is particularly useful for Beginners starting out to the field of Machine Learning. or Industry professionals and aspiring data scientists. or People who want to know how to write their clustering code.

Enroll now: Cluster Analysis : Unsupervised Machine Learning in Python

Summary

Title: Cluster Analysis : Unsupervised Machine Learning in Python

Price: $34.99

Average Rating: 4.29

Number of Lectures: 19

Number of Quizzes: 1

Number of Published Lectures: 19

Number of Published Quizzes: 1

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Describe the input and output of a clustering model

Prepare data with feature engineering techniques

Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models

Determine the optimal number of clusters

Use a variety of performance metrics such as Silhouette Score, Calinski-Harabasz Index and Davies-Bouldin Index.

Who Should Attend

Beginners starting out to the field of Machine Learning.

Industry professionals and aspiring data scientists.

People who want to know how to write their clustering code.

Target Audiences

Beginners starting out to the field of Machine Learning.

Industry professionals and aspiring data scientists.

People who want to know how to write their clustering code.

Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? Unsupervised machine learning is the underlying method behind a large part of this. Unsupervised machine learning algorithms analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without human intervention. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Clustering. This course provides the learners with the foundational knowledge to use Clustering models to create insights. You will become familiar with the most successful and widely used Clustering techniques, such as:

K-Means Clustering

Hierarchical Clustering

Mean Shift Clustering

DBSCAN : Density-Based Spatial Clustering of Applications with Noise

OPTICS : Ordering points to identify the clustering structure

Spectral Clustering

You will learn how to train clustering models to cluster and use performance metrics to compare different models. By the end of this course, you will be able to build machine learning models to make clusters using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!

Happy Learning.

Career Growth:

Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Artificial Intelligence

Lecture 3: Machine Learning

Lecture 4: Supervised Learning

Lecture 5: Supervised Learning: Classifications

Lecture 6: Supervised Learning: Regressions

Lecture 7: Unsupervised Learning

Lecture 8: Unsupervised Learning : Clustering

Lecture 9: Installation of Python Platform

Chapter 2: Building and Evaluating Clustering ML Models

Lecture 1: Important Terminologies

Lecture 2: K-Means Clustering

Lecture 3: Hierarchical Clustering

Lecture 4: Silhouette Score

Lecture 5: Calinski-Harabasz Index (Variance Ratio Criterion)

Lecture 6: Davies-Bouldin Index

Lecture 7: Mean Shift Clustering

Lecture 8: DBSCAN : Density Based Spatial Clustering of Applications with Noise

Lecture 9: OPTICS : Ordering points to identify the clustering structure

Lecture 10: Spectral Clustering

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

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3 stars: 2 votes

4 stars: 2 votes

5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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