Deliberate Practice in Learning: Impact and Techniques

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Related lectures (7)

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Introduction to Machine Learning: Course Overview and Basics

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

Introduction to Machine Learning

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Text Data Processing: Basics & Techniques

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Text Data Analysis: Basics and Techniques

Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.

Advanced Machine Learning: Feature Selection

Explores machine learning algorithms, feature selection techniques like FAST and BRIEF descriptors, and the limitations of deep learning.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.