May 1, 2024
Updated July 8, 2025
15 minute read
Machine learning (ML) is a subfield of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. ML algorithms are trained on data, and they can then make predictions or decisions based on that data. ML is used in a wide variety of applications, including image recognition, natural language processing, fraud detection, and medical diagnosis.
Why Learn Machine Learning?
There are many reasons to learn machine learning. First, ML is a rapidly growing field with a wide range of applications. As a result, there is a high demand for ML professionals. Second, ML can be used to solve complex problems that cannot be solved by traditional methods. Third, ML can help you to automate tasks and improve your efficiency.
How to Learn Machine Learning
There are many ways to learn machine learning. You can take online courses, read books, or attend workshops. You can also find many resources online, such as tutorials and documentation. If you are new to ML, it is important to start with the basics. Once you have a strong foundation, you can then move on to more advanced topics.
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Find a path to becoming a Machine Learning (ML). Learn more at:
OpenCourser.com/topic/ohixla/machine
Reading list
We've selected 15 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 (ML).
Provides a comprehensive overview of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is written by three of the leading researchers in deep learning.
Provides a broad overview of machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It is written by Andrew Ng, one of the leading researchers in machine learning.
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and tree-based methods. It is written by three of the leading researchers in statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and graphical models. It is written by one of the leading researchers in pattern recognition and machine learning.
Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It is written by a leading researcher in machine learning.
Provides a comprehensive overview of machine learning using Scikit-Learn and TensorFlow. It covers topics such as supervised learning, unsupervised learning, and deep learning. It is written by an experienced machine learning practitioner.
Provides a hands-on introduction to machine learning. It covers topics such as data wrangling, feature engineering, and model evaluation. It is written by an experienced machine learning practitioner.
Provides a practical introduction to machine learning, covering topics such as data wrangling, feature engineering, and model evaluation. It is written by two experienced machine learning practitioners.
Provides a gentle introduction to machine learning for people with no prior experience. It covers topics such as data wrangling, feature engineering, and model evaluation. It is written by a machine learning educator.
Provides a non-mathematical introduction to machine learning. It covers topics such as data wrangling, feature engineering, and model evaluation. It is written by a machine learning educator.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/ohixla/machine
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