May 1, 2024
Updated May 11, 2025
15 minute read
Delving into scikit-learn: A Comprehensive Guide
0mp5jo|
Find a path to becoming a scikit-learn. Learn more at:
OpenCourser.com/topic/0mp5jo/scikit
Reading list
We've selected nine 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
scikit-learn.
Provides a comprehensive introduction to machine learning using Python and the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Comprehensive introduction to machine learning. It covers all the major concepts of machine learning, including data preprocessing, feature engineering, model selection, and evaluation.
Comprehensive introduction to reinforcement learning. It covers all the major concepts of reinforcement learning, including Markov decision processes, value functions, and policy gradient methods.
Comprehensive introduction to bandit algorithms. It covers all the major concepts of bandit algorithms, including multi-armed bandits, contextual bandits, and Thompson sampling.
Provides a comprehensive introduction to statistical learning with sparsity. It covers all the major concepts of statistical learning with sparsity, including Lasso, Elastic Net, and Group Lasso.
Comprehensive introduction to causal inference in statistics. It covers all the major concepts of causal inference, including graphical models, counterfactuals, and causal effects.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It covers all the major concepts of information theory, inference, and learning algorithms, including entropy, mutual information, and Bayesian inference.
Provides a comprehensive introduction to convex optimization. It covers all the major concepts of convex optimization, including linear programming, quadratic programming, and semidefinite programming.
Provides a comprehensive introduction to PyTorch for deep learning. It covers all the major concepts of PyTorch for deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/0mp5jo/scikit
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