# Machine Learning

In this page we list useful resources to learn about machine learning.

Using these resources you will learn:

- [Linear regression](https://en.wikipedia.org/wiki/Linear_regression)
- [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression)
- [K-means clustering](https://en.wikipedia.org/wiki/K-means_clustering)
- [Multi-class classification](https://en.wikipedia.org/wiki/Multiclass_classification)
- [Multilayer Perceptron (MLP)](https://en.wikipedia.org/wiki/Multilayer_perceptron)
- [Convolutional Neural Networks](https://en.wikipedia.org/wiki/Convolutional_neural_network)
- [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning)
- [Machine Learning with PyTorch and Scikit-Learn](https://sebastianraschka.com/blog/2022/ml-pytorch-book.html)

## Resources

The resources are not provided in particular order.

- [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction)

- [AI For Everyone](https://www.coursera.org/learn/ai-for-everyone)

- [Introduction to Artificial Intelligence with Python by Harvard](https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python)

- [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)

- [Practical Deep Learning for Coders](https://course.fast.ai/)

- [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)

- [100 Days of ML Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code)

- [Deep Learning Book](https://www.deeplearningbook.org/)

- Stanford CS231n — Convolutional Neural Networks for Visual Recognition (YouTube)(https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&ab_channel=HaoDu) [GitHub](https://cs231n.github.io/convolutional-networks/)

- [ML-For-Beginners by Microsoft](https://github.com/microsoft/ML-For-Beginners/)

- [AI For Beginners by Microsoft](https://github.com/microsoft/AI-For-Beginners)

- [The Engineer’s Guide to Deep Learning](https://www.interdb.jp/dl/index.html)

If you would like to suggest a resource, email us aup@amd.com

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