To start learning pattern recognition, begin with its mathematical foundations, including linear algebra, probability, and optimization. Learn algorithms like k-nearest neighbors (k-NN), support vector machines (SVMs), and decision trees for supervised tasks.
Use Python libraries like Scikit-learn to implement basic pattern recognition models on datasets like MNIST or CIFAR-10. Gradually explore advanced methods involving deep learning and neural networks.
Study resources like Pattern Recognition and Machine Learning by Christopher Bishop and take online courses from platforms like Coursera or edX. Working on small projects will reinforce theoretical concepts.