Data Processing, Machine Learning, and Model Evaluation

Overview

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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 © 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.

Syllabus

Data Processing and Preparation

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.

Modeling and Evaluation

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.

Model Validation and Deployment

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.

Unsupervised Machine Learning

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.