Machine-Learning-Tutorials

This repository is a curated reference list of tutorials, articles, courses, and other learning resources for machine learning and deep learning. It is not a library or tool — it is a structured collection of links organised by topic to help people learn or deepen their understanding of AI concepts.

Machine learning is a field of computer science where programs learn patterns from data rather than being explicitly programmed with rules. Deep learning is a subset of machine learning that uses multi-layered neural networks (software systems loosely modelled on how brains work) to handle tasks like image recognition, language understanding, and more.

The list is organised into clearly labelled topic sections. These cover introductory courses (including university-level lecture series), interview preparation resources, classical techniques such as linear regression, logistic regression, decision trees, random forests, and support vector machines, and deep learning-specific topics such as convolutional neural networks (used for image tasks), recurrent neural networks and LSTM (used for sequences and language), and autoencoders. There are also sections on natural language processing, computer vision, reinforcement learning, statistics, Bayesian methods, and model validation approaches like cross-validation.

You would use this as a study guide if you are starting to learn machine learning and want to find high-quality courses and readings without having to search from scratch, or if you are preparing for data science interviews and need a structured refresher. The full README is longer than what was provided.