Evaluating a Data Mining Model from Pluralsight

This course covers the important techniques in model evaluation for some of the most popular types of data mining techniques. These techniques range from association rules learning to clustering, regression, and classification.

Data Mining is an umbrella term used for techniques that find patterns in large datasets. Thus, data mining can effectively be thought of as the application of machine learning techniques to big data.

In this course, Evaluating a Data Mining Model, you will gain the ability to answer the two most important questions that every practitioner of data mining must answer - is a particular model valid for this data? And, if yes, what is that model telling us?

First, you will learn that evaluating model fit and interpreting model results are key steps in the data mining process. Next, you will discover how association rules learning - a classic data mining technique - is implemented and evaluated.

Finally, you will round out your knowledge by seeing how the popular ML solution techniques - regression, classification, and clustering - can be implemented and evaluated for fit.

When you’re finished with this course, you will have the skills and knowledge to implement data mining techniques, evaluate them for model fit, and then intelligently interpret their findings.

What's inside

Syllabus

Course Overview

Evaluating the Effectiveness of a Clustering Model

Evaluating the Effectiveness of Association Rule Mining

Evaluating the Effectiveness of Regression Models

Evaluating the Effectiveness of Classification Models

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Touches on multiple data mining techniques, which is beneficial for learners who want to gain experience with multiple data mining techniques

Explores the application of machine learning to big data, which is a key concept in data science

Taught by Janani Ravi, who has established credibility in the data mining field with their work on data mining for recommender systems

Provides a solid foundation in model evaluation for data mining beginners, improving their ability to assess and derive insights from data mining projects

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 Evaluating a Data Mining Model with these activities:

Organize and review notes and course materials

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Prepare for the course by organizing and reviewing existing notes and materials to refresh your knowledge.

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Gather notes, assignments, and other materials from previous courses or self-study.
Organize these materials into a structured and easily accessible format.

Read 'Data Mining Techniques' by Michael Berry and Gordon Linoff

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Expand your knowledge of data mining techniques by exploring advanced concepts in this comprehensive book.

View Data Mining Techniques: For Marketing, Sales,... on Amazon

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Read chapters relevant to the techniques covered in this course.
Take notes and summarize key concepts related to model evaluation.

Practice supervised model training

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Refresh your understanding of supervised machine learning techniques to gain a strong foundation for this course.

Browse courses on Supervised Learning

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Review core concepts of supervised machine learning such as classification and regression.
Train a supervised learning model using a programming language like Python or R.

Four other activities

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Participate in a study group with classmates

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Enhance your understanding through discussions and shared insights with fellow classmates.

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Form or join a study group with other students in the course.
Meet regularly to discuss course material, solve problems, and share perspectives.

Solve practice problems on model evaluation metrics

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Reinforce your understanding of model evaluation metrics through targeted practice.

Browse courses on Model Evaluation

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Attempt practice problems involving common model evaluation metrics like accuracy, precision, recall, and F1-score.

Learn advanced techniques for association rule mining

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Extend your knowledge of association rule mining techniques to explore advanced concepts covered in this course.

Browse courses on Association Rule Mining

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Explore online tutorials on topics like multi-level association rule mining and sequential pattern mining.
Implement an advanced association rule mining technique in a programming language.

Develop a data mining project using a chosen technique

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Solidify your learning by applying data mining techniques to a real-world problem.

Browse courses on Model Implementation

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Identify a problem or dataset suitable for data mining analysis.
Select a data mining technique from what you've learned in the course.
Implement the data mining solution and evaluate its effectiveness.

Career center

Learners who complete Evaluating a Data Mining Model will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is a professional who utilizes data mining techniques to provide insightful information to their organization. This course helps build a solid foundation in evaluating data mining models. The course will increase your efficiency and efficacy as a Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect and analyze raw data. They may also carry out statistical modeling and provide valuable insights to management to help facilitate future decisions. This course is essential for the Data Analyst. It teaches how to evaluate data mining models and how to interpret the results from these models.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers apply engineering principles to build, deploy, and maintain machine learning systems. These systems may apply data mining techniques. This course is useful for Machine Learning Engineers who want to excel at evaluating the effectiveness of their models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts evaluate and interpret data to help their organization make better decisions and identify areas of improvement. This course may be helpful for Business Analysts, as it can teach them how to evaluate and interpret data mining models.

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Market Researcher

Market Researchers conduct research on markets, products, and services. This research can involve collecting and analyzing data. This course may be useful for Market Researchers, as it can teach them how to evaluate data mining models.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business, industry, and government. This course may be helpful for Operations Research Analysts, as it can teach them how to evaluate data mining models.

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Data Warehouse Analyst

Data Warehouse Analysts design, build, and maintain data warehouses. This course may be helpful for Data Warehouse Analysts, as it can teach them how to evaluate data mining models.

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Data Architect

Data Architects help organizations design and manage their data. This course may be helpful for Data Architects, as it can teach them how to evaluate data mining models.

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Data Miner

Data Miners use data mining techniques to extract knowledge from large datasets. This course is useful for Data Miners, as it teaches how to evaluate the effectiveness of data mining models, a critical skill for this role.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course may be useful for Quantitative Analysts, as it can teach them how to evaluate data mining models.

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Credit Risk Analyst

Credit Risk Analysts evaluate the creditworthiness of borrowers. This course may be helpful for Credit Risk Analysts, as it can teach them how to evaluate data mining models.

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Insurance Analyst

Insurance Analysts evaluate insurance risks and develop insurance policies. This course may be helpful for Insurance Analysts, as it can teach them how to evaluate data mining models.

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Fraud Analyst

Fraud Analysts investigate and prevent fraud. This course may be helpful for Fraud Analysts, as it can teach them how to evaluate data mining models.

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Statistician

Statisticians collect, analyze, interpret, and present data. This course may be helpful for Statisticians, as it can teach them how to evaluate data mining models.

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Economist

Economists study the production, distribution, and consumption of goods and services. This course may be helpful for Economists, as it can teach them how to evaluate data mining models.

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