Scikit-Learn Documentation:
Scikit-Learn is a popular machine learning library for Python. The official documentation includes comprehensive guides, tutorials, and examples to get you started.
TensorFlow Tutorials:
TensorFlow is an open-source machine learning framework developed by Google. TensorFlow offers a wealth of resources, including tutorials and guides for various aspects of machine learning.
Kaggle:
Kaggle is a platform for data science and machine learning. It offers courses, datasets, competitions, and kernels (code notebooks) that provide hands-on experience in ML.
Coursera:
While Coursera offers paid courses, many top universities provide free auditing options for their machine learning courses. For example, "Machine Learning" by Andrew Ng is a highly recommended course.
edX:
Similar to Coursera, edX offers free auditing options for courses from universities and institutions. You can find machine learning courses from top universities.
Fast.ai:
Fast.ai offers practical deep learning courses with a focus on deep learning and neural networks. Their courses are free and provide hands-on experience.
Stanford University's CS231n:
This is a popular course on Convolutional Neural Networks (CNNs) for computer vision. The course materials, including video lectures and notes, are freely available.
Books:
There are excellent free books on machine learning, such as "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili. You can often find these books online for free.
Youtube Channels:
There are various YouTube channels that offer free tutorials on machine learning. Channels like "sentdex" and "Data School" provide informative video tutorials.
Github Repositories:
Many individuals and organizations share machine learning projects, tutorials, and resources on GitHub. You can explore various ML repositories for practical examples.