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Free
CompTIA DataAI (Formerly DataX) DY0-001 Exam Preparation Course
What you'll learn
* Apply hypothesis testing, probability distributions and advanced statistical methods
* Use linear algebra, matrix operations and calculus concepts in data-science models
* Conduct exploratory data analysis and identify complex dataset problems
* Engineer, transform, enrich, standardize and augment analytical features
* Design, compare, evaluate and document data-science models
* Apply supervised, unsupervised, ensemble and deep-learning techniques
* Interpret model metrics and address imbalance, overfitting, leakage and drift
* Build reproducible data pipelines and support MLOps deployment and monitoring
* Apply NLP, computer vision, optimization and other specialized AI methods
* Communicate model outcomes while applying privacy, security and responsible-AI principles
Trending
Free
CompTIA DataAI (Formerly DataX) DY0-001 Exam Preparation Course
What you'll learn
* Apply hypothesis testing, probability distributions and advanced statistical methods
* Use linear algebra, matrix operations and calculus concepts in data-science models
* Conduct exploratory data analysis and identify complex dataset problems
* Engineer, transform, enrich, standardize and augment analytical features
* Design, compare, evaluate and document data-science models
* Apply supervised, unsupervised, ensemble and deep-learning techniques
* Interpret model metrics and address imbalance, overfitting, leakage and drift
* Build reproducible data pipelines and support MLOps deployment and monitoring
* Apply NLP, computer vision, optimization and other specialized AI methods
* Communicate model outcomes while applying privacy, security and responsible-AI principles
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All CompTIA DataAI: DY0-001 Courses
Free
CompTIA DataAI (Formerly DataX) DY0-001 Exam Preparation Course
Prepare for the CompTIA DataAI DY0-001 Exam Validate advanced data …
What you'll learn
* Apply hypothesis testing, probability distributions and advanced statistical methods
* Use linear algebra, matrix operations and calculus concepts in data-science models
* Conduct exploratory data analysis and identify complex dataset problems
* Engineer, transform, enrich, standardize and augment analytical features
* Design, compare, evaluate and document data-science models
* Apply supervised, unsupervised, ensemble and deep-learning techniques
* Interpret model metrics and address imbalance, overfitting, leakage and drift
* Build reproducible data pipelines and support MLOps deployment and monitoring
* Apply NLP, computer vision, optimization and other specialized AI methods
* Communicate model outcomes while applying privacy, security and responsible-AI principles
