Building Clustering Models with scikit-learn from Pluralsight

This course covers several important techniques used to implement clustering in scikit-learn, including the K-means, mean-shift and DBScan clustering algorithms, as well as the role of hyperparameter tuning, and performing clustering on image data.

Clustering is an extremely powerful and versatile unsupervised machine learning technique that is especially useful as a precursor to applying supervised learning techniques like classification. In this course, Building Clustering Models with scikit-learn, you will gain the ability to enumerate the different types of clustering algorithms and correctly implement them in scikit-learn. First, you will learn what clustering seeks to achieve, and how the ubiquitous k-means clustering algorithm works under the hood. Next, you will discover how to implement other techniques such as DBScan, mean-shift, and agglomerative clustering. You will then understand the importance of hyperparameter tuning in clustering, such as identifying the correct number of clusters into which your data ought to be partitioned. Finally, you will round out the course by implementing clustering algorithms on image data - an especially common use-case. When you are finished with this course, you will have the skills and knowledge to select the correct clustering algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.

What's inside

Syllabus

Course Overview

Building a Simple Clustering Model in scikit-learn

Performing Clustering Using Multiple Techniques

Hyperparameter Tuning for Clustering Models

Applying Clustering to Image Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops clustering techniques and applications, which are core skills for machine learning specialists

Teaches scikit-learn, which is standard in data analysis

Explores clustering on image data, which is a highly relevant use case for computer vision

This course requires some prior knowledge of Python and machine learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Clustering Models with scikit-learn with these activities:

Clustering Basics

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Review basic concepts and theory of clustering to prepare for the course.

Browse courses on Clustering Algorithms

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Review definitions of clustering and its techniques.
Examine and categorize a provided dataset based on different clustering options.

Clustering Project Discussion

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Engage in discussions with peers to share knowledge, troubleshoot issues, and deepen understanding of clustering concepts.

Browse courses on Clustering Algorithms

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Form study groups or join online forums related to clustering.
Present clustering projects or ideas to receive feedback and suggestions.
Collaborate on data analysis, algorithm selection, and interpretation of results.

Scikit-Learn K-Means Exercise

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Practice implementing K-Means clustering using the Scikit-Learn library.

Browse courses on scikit-learn

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Install Scikit-Learn and import necessary modules.
Load and explore a sample dataset.
Apply the K-Means clustering algorithm to the dataset.
Evaluate the clustering results and adjust parameters as needed.

Two other activities

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Hyperparameter Tuning for Clustering

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Explore techniques for optimizing hyperparameters in clustering models to enhance performance.

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Understand the role of hyperparameters in clustering algorithms.
Identify common hyperparameters and their impact on clustering results.
Implement hyperparameter tuning methods such as grid search or random search.
Compare the results of different hyperparameter configurations and select the optimal settings.

Cluster Analysis Case Study

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Apply clustering techniques to a real-world dataset and present the findings in a comprehensive report.

Browse courses on Clustering Algorithms

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Gather a suitable dataset for clustering analysis.
Preprocess and explore the dataset.
Implement and evaluate different clustering algorithms.
Interpret and visualize the clustering results.
Write a report summarizing the findings and insights.

Career center

Learners who complete Building Clustering Models with scikit-learn will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to extract meaningful insights that can help businesses make informed decisions. Clustering techniques are commonly used in data analysis to identify patterns and group similar data points together. This course covers several clustering algorithms that can be used to solve a variety of data analysis problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for data analysis and to make informed data-driven decisions.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and deploying machine learning models that can solve real-world problems. Clustering is a fundamental technique used in machine learning for unsupervised learning tasks, such as customer segmentation and image recognition. This course covers several clustering algorithms that can be used to build machine learning models for a variety of applications. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for machine learning and to develop and deploy successful machine learning models.

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Data Scientist

Data Scientists are responsible for using data to solve business problems. Clustering is a powerful technique that can be used to identify patterns and trends in data, which can be used to improve decision-making. This course covers several clustering algorithms that can be used to solve a variety of data science problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for data science and to solve real-world business problems.

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Statistician

Statisticians are responsible for collecting, analyzing, interpreting, and presenting data. Clustering is a commonly used statistical technique for identifying patterns and relationships in data. This course covers several clustering algorithms that can be used to solve a variety of statistical problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for statistical analysis and to make informed data-driven decisions.

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Market Researcher

Market Researchers are responsible for collecting, analyzing, and interpreting data about markets and consumers. Clustering is a technique that can be used to identify customer segments and to understand consumer behavior. This course covers several clustering algorithms that can be used to solve a variety of market research problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for market research and to make informed marketing decisions.

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Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. Clustering is a technique that can be used to identify patterns and trends in data, which can be used to improve business operations. This course covers several clustering algorithms that can be used to solve a variety of business analysis problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for business analysis and to make informed business decisions.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts are responsible for analyzing financial data and making investment recommendations. Clustering is a technique that can be used to identify patterns and trends in financial data, which can be used to make informed investment decisions. This course covers several clustering algorithms that can be used to solve a variety of financial analysis problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for financial analysis and to make informed investment decisions.

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Project Manager

Project Managers are responsible for planning, executing, and closing projects. Clustering is a technique that can be used to identify patterns and trends in project data, which can be used to improve project management. This course covers several clustering algorithms that can be used to solve a variety of project management problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for project management and to successfully manage projects.

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Risk Analyst

Risk Analysts are responsible for identifying and assessing risks. Clustering is a technique that can be used to identify patterns and trends in data, which can be used to assess risk. This course covers several clustering algorithms that can be used to solve a variety of risk analysis problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for risk analysis and to make informed risk management decisions.

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Sales Manager

Sales Managers are responsible for leading and managing sales teams. Clustering is a technique that can be used to identify customer segments and to understand customer behavior. This course covers several clustering algorithms that can be used to solve a variety of sales management problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for sales management and to lead and manage successful sales teams.

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Marketing Manager

Marketing Managers are responsible for planning, executing, and evaluating marketing campaigns. Clustering is a technique that can be used to identify customer segments and to understand customer behavior. This course covers several clustering algorithms that can be used to solve a variety of marketing management problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for marketing management and to plan, execute, and evaluate successful marketing campaigns.

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Product Manager

Product Managers are responsible for managing the development and launch of new products. Clustering is a technique that can be used to identify customer needs and to understand market trends. This course covers several clustering algorithms that can be used to solve a variety of product management problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for product management and to successfully manage the development and launch of new products.

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Human Resources Manager

Human Resources Managers are responsible for managing the human resources department of an organization. Clustering is a technique that can be used to identify employee groups and to understand employee behavior. This course covers several clustering algorithms that can be used to solve a variety of human resources management problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for human resources management and to successfully manage the human resources department of an organization.

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Operations Manager

Operations Managers are responsible for planning, executing, and controlling operations. Clustering is a technique that can be used to identify patterns and trends in operational data, which can be used to improve operations. This course covers several clustering algorithms that can be used to solve a variety of operations management problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for operations management and to successfully plan, execute, and control operations.

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Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. Clustering is a technique that can be used to identify patterns and trends in software data, which can be used to improve software development and maintenance. This course covers several clustering algorithms that can be used to solve a variety of software engineering problems. By taking this course, you will gain the skills and knowledge necessary to effectively use clustering algorithms for software engineering and to successfully design, develop, and maintain software applications.

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