Nigeria's free digital learning hub. Videos, lecture notes, practice questions and leaderboards aligned with CCMAS, NBTE, NCCE, WAEC, NECO, IJMB and JAMB curricula.
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Nigeria's free digital learning hub. Videos, lecture notes, practice questions and leaderboards aligned with CCMAS, NBTE, NCCE, WAEC, NECO, IJMB and JAMB curricula.
The foundations of machine learning focus on the core principles, theories, and techniques that enable computers to learn patterns from data and make predictions or decisions without explicit programming. These foundations combine elements from statistics, mathematics (such as linear algebra and probability), and computer science to create models that can generalize from examples. Key concepts include supervised and unsupervised learning, model training and evaluation, overfitting and underfitting, and optimization methods like gradient descent. Understanding these basics provides the groundwork for more advanced topics, allowing practitioners to design, analyze, and improve algorithms that power applications such as image recognition, natural language processing, and recommendation systems.
This lecture is part of CSC316 MACHINE LEARNING in the Computer Science department. The course has 30 recorded lectures across 10 series, all free to watch.
