May 1, 2024
3 minute read
Machine Learning Basics, an essential component of artificial intelligence (AI), has revolutionized numerous industries by providing computers with the ability to “learn” from data without explicit programming. It empowers machines to identify patterns and make predictions, enabling a wide range of applications.
Why Learn Machine Learning Basics?
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Find a path to becoming a Machine Learning Basics. Learn more at:
OpenCourser.com/topic/vvs288/machine
Reading list
We've selected 12 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
Machine Learning Basics.
Deep Learning comprehensive textbook on deep learning. The book covers the mathematical foundations of deep learning, as well as practical techniques for building and training deep learning models.
Machine Learning Yearning is written by Andrew Ng, a leading researcher in the field. The book provides a comprehensive overview of machine learning concepts, from fundamental principles to advanced techniques.
Artificial Intelligence: A Modern Approach classic textbook on artificial intelligence. The book covers a wide range of topics, from logic and reasoning to machine learning and computer vision.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow practical guide to machine learning using popular Python libraries. The book covers a wide range of topics, from data preprocessing to model evaluation.
Machine Learning: A Probabilistic Perspective provides a comprehensive overview of machine learning from a probabilistic perspective. The book covers a wide range of topics, from Bayesian inference to Gaussian processes.
Machine Learning: An Algorithmic Perspective provides a comprehensive overview of machine learning algorithms. The book covers the theoretical foundations of machine learning, as well as practical techniques for implementing machine learning algorithms.
Machine Learning with Python provides a comprehensive overview of machine learning using the Python programming language. The book covers a wide range of topics, from data preprocessing to model evaluation.
Mathematics for Machine Learning provides a comprehensive overview of the mathematical foundations of machine learning. The book covers a wide range of topics, from linear algebra to probability theory.
Reinforcement Learning: An Introduction provides a comprehensive overview of reinforcement learning. The book covers the theoretical foundations of reinforcement learning, as well as practical techniques for implementing reinforcement learning algorithms.
Machine Learning with R provides a comprehensive overview of machine learning using the R programming language. The book covers a wide range of topics, from data preprocessing to model evaluation.
Machine Learning for Dummies beginner-friendly introduction to machine learning. The book covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
Machine Learning for Hackers practical guide to machine learning for hackers. The book covers a wide range of topics, from data preprocessing to model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/vvs288/machine
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