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Catalog / All levels / Exam 5 - CompTIA DataAI Certification
Mammoth Club All levels 3 sections 20 lectures

Exam 5 - CompTIA DataAI Certification

Test your knowledge with 100+ questions from the Machine Learning section of the CompTIA DataAI (formerly named DataX) certification. Find out if you’re ready to ace the exam — can you apply foundational machine-learning concepts? Can you apply appropriate statistical supervised machine-learning concepts? Can you apply tree-based supervised machine-learning concepts? And much more!

MC
Created by Team Mammoth
01
Skill level
All levels
02
Sections
3
03
Lectures
20
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
3
Sections
✓
20
Lectures
✓
20
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

3 sections · 20 lectures
+ Exam - Apply foundational machine learning concepts 9 lectures
Loss Function, Variance Minimization, and the Bias-Variance Tradeoff Locked
Overfitting and Underfitting Locked
Variable and Feature Selection Locked
Class Imbalance and Mitigations Locked
Regularization and Cross-Validation Locked
The Curse of Dimensionality and Occam’s Razor Locked
In Sample vs. Out of Sample Locked
Ensemble Models Locked
Hyperparameter Tuning Locked
+ Exam - Apply appropriate statistical supervised machine learning concepts 7 lectures
Linear Regression and Ordinary Least Squares (OLS) Locked
Ridge & LASSO Regression Locked
Elastic Net Locked
Logistic Regression Locked
Linear & Quadratic Discriminant Analysis Locked
Association Rules Locked
Naive Bayes Locked
+ Exam - Apply tree-based supervised machine learning concepts 4 lectures
Decision Trees & Random Forest Locked
Bag and Boost Locked
Gradient Boosting & XGBoost Locked
Model Selection and Hyperparameter Tuning Locked

Ready to start building?

Test your knowledge with 100+ questions from the Machine Learning section of the CompTIA DataAI (formerly named DataX) certification. Find out if you’re ready to ace the exam — can you apply foundational machine-learning concepts? Can you apply appropriate statistical supervised machine-learning concepts? Can you apply tree-based supervised machine-learning concepts? And much more!

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