Undergraduate Certificate in Machine Learning Semi-Supervised Learning
-- ViewingNowThe Undergraduate Certificate in Machine Learning: Semi-Supervised Learning is a career-enhancing course designed to equip learners with essential skills in machine learning. This certificate program focuses on semi-supervised learning, a powerful approach that combines both unsupervised and supervised learning techniques, enabling machines to learn from limited labeled data.
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โข Introduction to Machine Learning · Overview of supervised, unsupervised, and
semi-supervised learning, common algorithms, and applications.
โข Data Preprocessing · Data cleaning, normalization, and feature engineering.
โข Semi-Supervised Learning Fundamentals · Introduction to SSL techniques,
advantages, and limitations.
โข Self-training · Pseudo-labeling, entropy-based methods, and confidence
thresholding.
โข Multi-view Training · Co-training, multi-view bootstrapping, and
tri-training.
โข Graph-based Semi-Supervised Learning · Graph construction, label
propagation, and graph-based SSL algorithms.
โข Deep Semi-Supervised Learning · Semi-supervised deep learning models,
generative adversarial networks (GANs), and deep belief networks (DBNs).
โข Evaluation Metrics for Semi-Supervised Learning · Accuracy, precision,
recall, F1 score, and area under the curve (AUC).
โข Real-World Applications of Semi-Supervised Learning · Case studies
highlighting the use of SSL in industries such as healthcare, finance, and
marketing.
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