May 1, 2024
3 minute read
Classification Methods is a subfield of machine learning that deals with the task of assigning a label to a given input data point. Classification is used to build models that can predict the outcome of a given input, such as whether a loan application will be approved or denied, or whether a patient has a particular disease. Classification can be used in a variety of applications, including fraud detection, customer segmentation, and medical diagnosis.
Difficulty of Classification
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Find a path to becoming a Classification Methods. Learn more at:
OpenCourser.com/topic/vqhsc1/classification
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Classification Methods.
Provides a comprehensive overview of deep learning, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for both beginners and advanced readers.
Provides a comprehensive overview of statistical learning, including supervised and unsupervised learning, classification, and regression. It is suitable for both beginners and advanced readers.
Provides a practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including classification, regression, clustering, and deep learning.
Provides a comprehensive overview of pattern recognition and machine learning, including supervised and unsupervised learning, classification, and regression. It is suitable for both beginners and advanced readers.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, including classification, regression, clustering, and deep learning.
Provides a comprehensive overview of probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov random fields, and Kalman filters.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy optimization.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers a wide range of topics, including Bayesian inference, graphical models, and Markov chain Monte Carlo.
Provides a high-level overview of machine learning, including supervised and unsupervised learning, classification, and regression. It is written by Andrew Ng, one of the pioneers of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/vqhsc1/classification
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