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⇱ Data Processing, Machine Learning, and Model Evaluation | Coursera


Data Processing, Machine Learning, and Model Evaluation

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Data Processing, Machine Learning, and Model Evaluation

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Prepare and transform datasets using data cleaning and preprocessing techniques

  • Build and evaluate machine learning models using appropriate metrics

  • Apply model validation methods to improve prediction reliability

Details to know

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Add to your LinkedIn profile

Recently updated!

April 2026

Assessments

4 assignments

Taught in English

Build your subject-matter expertise

This course is part of the CompTIA DataX Study Guide Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course teaches you the essential skills required to process and prepare data, model, and evaluate machine learning models. Data processing is a fundamental step in extracting valuable insights from raw data and is crucial in professional data science and machine learning careers.

By mastering these techniques, you will enhance your ability to prepare and clean data, build effective machine learning models, and evaluate their performance. These skills are vital for ensuring that your models are accurate, reliable, and ready for deployment in real-world scenarios. The course bridges theory with real-world applications by combining hands-on data processing exercises with machine learning techniques. This approach ensures learners not only understand theoretical concepts but also apply them effectively in practical situations. This course is ideal for aspiring data scientists, machine learning engineers, and professionals looking to strengthen their modeling and evaluation skills. A basic understanding of data science concepts will help, though no advanced experience is required. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. From CompTIA DataX Study Guide Copyright Β© 2024 by John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Used by arrangement with John Wiley & Sons, Inc.

In this section, we cover essential data transformation, enrichment, and cleaning techniques, including encoding, normalization, joining, and handling data quality issues to prepare datasets for robust analytics and machine learning applications.

What's included

1 video8 readings1 assignment

1 videoβ€’Total 1 minute
  • Data Processing and Preparation - Overview Videoβ€’1 minute
8 readingsβ€’Total 100 minutes
  • Introductionβ€’15 minutes
  • Transformation Functionsβ€’15 minutes
  • Pivotingβ€’15 minutes
  • Joinsβ€’15 minutes
  • Data Cleaningβ€’10 minutes
  • Addressing Duplicate Dataβ€’10 minutes
  • Handling Class Imbalanceβ€’10 minutes
  • Exam Essentialsβ€’10 minutes
1 assignmentβ€’Total 10 minutes
  • Data Processing Fundamentalsβ€’10 minutes

In this section, we construct and evaluate predictive models using regressors, classifiers, and temporal methods, assess performance with metrics like RMSE and F1 score, and explore concepts such as bias-variance trade-off and hyperparameter tuning.

What's included

1 video8 readings1 assignment

1 videoβ€’Total 1 minute
  • Modeling and Evaluation - Overview Videoβ€’1 minute
8 readingsβ€’Total 85 minutes
  • Introductionβ€’10 minutes
  • The Challenge of Censoring in Survival Analysisβ€’10 minutes
  • Model Design Conceptsβ€’10 minutes
  • The Law of Parsimony (Occam's Razor)β€’10 minutes
  • Model Evaluationβ€’10 minutes
  • Accuracyβ€’15 minutes
  • Real World Scenario - Choosing the Appropriate Performance Metricβ€’10 minutes
  • Exam Essentialsβ€’10 minutes
1 assignmentβ€’Total 10 minutes
  • Evaluating Machine Learning Modelsβ€’10 minutes

In this section, we evaluate model performance using key metrics and constraints, compare deployment strategies including MLOps, and discuss effective communication of model outcomes to stakeholders for practical data science applications.

What's included

1 video7 readings1 assignment

1 videoβ€’Total 1 minute
  • Model Validation and Deployment - Overview Videoβ€’1 minute
7 readingsβ€’Total 70 minutes
  • Introductionβ€’10 minutes
  • Real World Scenario - Developing a Product Recommendation Modelβ€’10 minutes
  • Residual Plotβ€’10 minutes
  • Real World Scenarioβ€’10 minutes
  • Cloud Deploymentβ€’10 minutes
  • Machine Learning Operations (MLOps)β€’10 minutes
  • Testingβ€’10 minutes
1 assignmentβ€’Total 10 minutes
  • Model Validation and Deployment Fundamentalsβ€’10 minutes

In this section, we explore association rules, focusing on their structure, interpretation of itemsets, antecedents, and consequents, and how actionable patterns in transactional data inform data-driven decisions.

What's included

1 video7 readings1 assignment

1 videoβ€’Total 1 minute
  • Unsupervised Machine Learning - Overview Videoβ€’1 minute
7 readingsβ€’Total 70 minutes
  • Introductionβ€’10 minutes
  • Liftβ€’10 minutes
  • The Average Silhouette Methodβ€’10 minutes
  • Density-Based Clusteringβ€’10 minutes
  • Singular Value Decompositionβ€’10 minutes
  • Recommender Systemsβ€’10 minutes
  • Exam Essentialsβ€’10 minutes
1 assignmentβ€’Total 10 minutes
  • Exploring Unsupervised Learning Methodsβ€’10 minutes

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Instructor

John Wiley & Sons
121 Coursesβ€’7,217 learners

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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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