Advanced Certificate in AI Machine Learning Basics: Fundamentals Explained
-- ViewingNowThe Advanced Certificate in AI Machine Learning Basics: Fundamentals Explained is a comprehensive course that equips learners with essential skills in AI and Machine Learning. This certification is crucial in today's data-driven world, where businesses increasingly rely on AI technologies for decision-making and automation.
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• Introduction to AI & Machine Learning: Understanding the basics of Artificial Intelligence (AI) and Machine Learning (ML), their differences, and applications.
• Mathematics for Machine Learning: Diving into essential mathematical concepts such as linear algebra, calculus, probability, and statistics used in ML.
• Data Preprocessing: Learning to clean, transform, and prepare real-world datasets for ML model training.
• Supervised Learning: Mastering algorithms like linear regression, logistic regression, and support vector machines (SVM) for regression and classification tasks.
• Unsupervised Learning: Exploring clustering, dimensionality reduction, and association rule learning techniques for unlabelled data.
• Neural Networks: Delving into the structure, types, and training techniques of artificial neural networks.
• Deep Learning: Understanding deep neural networks, including convolutional neural networks (CNN) and recurrent neural networks (RNN), with practical applications.
• Reinforcement Learning: Learning about the basics of reinforcement learning and its applications, such as Q-learning and Deep Q Networks (DQN).
• Evaluating ML Models: Measuring model performance using appropriate metrics, cross-validation, and bias-variance trade-offs.
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