Global Certificate in Classification Analysis for a Data-Driven World
-- ViewingNowThe Global Certificate in Classification Analysis for a Data-Driven World is a comprehensive course designed to equip learners with essential skills in data analysis. This certification focuses on classification, a fundamental aspect of data science, and teaches students how to make informed decisions based on data.
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⢠Introduction to Classification Analysis: Defining classification analysis, its importance, and applications in data-driven decision making.
⢠Data Preprocessing: Data cleaning, wrangling, and transformation techniques for classification analysis.
⢠Feature Selection and Engineering: Identifying relevant features, dimensionality reduction, and creating new features for improved classification.
⢠Supervised Learning Algorithms: Overview of machine learning algorithms for classification, including logistic regression, decision trees, and support vector machines.
⢠Model Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC curves, and other evaluation metrics for assessing classification models.
⢠Model Tuning and Validation: Techniques for hyperparameter tuning, cross-validation, and model selection to improve performance.
⢠Unsupervised Learning for Classification: Clustering algorithms and anomaly detection as alternatives or complements to supervised learning.
⢠Ethics in Classification Analysis: Understanding and addressing potential biases, fairness, and transparency issues in classification models.
⢠Deploying Classification Models: Best practices for deploying, monitoring, and updating classification models in production environments.
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