Machine Learning Fundamentals

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Related lectures (32)

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Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Visual Intelligence: Machines and Minds

Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Smart, Connected Products

Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.