Artificial Prism
machine-learning
Page 1
Understanding AI Model Evaluation and Metrics
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An introduction to Bayesian inference and probabilistic models.
Exploring the critical issues of bias and fairness in artificial intelligence, their causes, and potential mitigation strategies.
Exploring the powerful synergy between big data and artificial intelligence and its impact on various industries.
Introduction to fundamental concepts in computer vision.
A deep dive into explainable and interpretable AI, their differences, importance, and techniques.
A comprehensive guide to feature engineering and selection techniques for machine learning.
An overview of gradient descent and related optimization techniques used in machine learning.
Understanding and implementing hyperparameter tuning techniques in machine learning.
An overview of Natural Language Processing and its applications.
An introduction to neural networks and deep learning concepts.
Introduction to the fundamentals of reinforcement learning.
A comparison of supervised and unsupervised learning techniques.
An overview of transfer learning and the use of pretrained models.
