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Machine Learning Essentials (Free Seminar) with Kimberly Fessel, Dr Kim Data | Data Science Consultancy

https://instats.org/seminar/machine-learning-essentials-free-semina2?utm_source=dresa

Description:

This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.

Start: Thursday, 06 February 2025 @ 03:00

End: Thursday, 06 February 2025 @ 04:00

Timezone: Sydney

Learning Objectives:

Topics Covered
Model Concept , Supervised vs Unsupervised , Underfitting vs Overfitting , Regularization Role , Linear Regression , Logistic Regression , Decision Trees , Neural Networks Overview

Eligibility:
  • Open to all

Organiser: Kimberly Fessel, Dr Kim Data | Data Science Consultancy

Contact: info@instats.org

Host institution: Instats

Cost Basis: Free to all

Machine Learning Essentials (Free Seminar) with Kimberly Fessel, Dr Kim Data | Data Science Consultancy https://dresa.org.au/events/machine-learning-essentials-free-seminar-with-kimberly-fessel-dr-kim-data-data-science-consultancy This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks. 2025-02-06 03:00:00 UTC 2025-02-06 04:00:00 UTC Kimberly Fessel, Dr Kim Data | Data Science Consultancy Instats info@instats.org [] [] [] open_to_all []