Data Processing, Machine Learning, and Model Evaluation
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Data Processing, Machine Learning, and Model Evaluation
This course is part of CompTIA DataX Study Guide Specialization
Instructor: Wiley Skills Network
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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
Skills you'll gain
- Data Science
- Applied Machine Learning
- Data Preprocessing
- Technical Communication
- Supervised Learning
- Model Evaluation
- Model Optimization
- Data Analysis
- Model Training
- Data Processing
- Predictive Modeling
- Machine Learning
- Artificial Neural Networks
- Unsupervised Learning
- Data Transformation
- Data Cleansing
- MLOps (Machine Learning Operations)
Tools you'll learn
Details to know
April 2026
4 assignments
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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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