Dive deep into Graph Neural Networks (GNN) implementation with Python in this comprehensive tutorial. Learn technical details and gain hands-on experience using NetworkX, PyG (pytorch_geometric), and matplotlib libraries. Explore graph representations, node embedding, message passing, and GNN explainers. Practice node classification using MLP and GNN, visualize graphs with NetworkX and tSNE, and understand various graph datasets available in PyG. Master concepts like adjacency matrices, convolution on graphs, and message passing through practical Jupyter notebook exercises and a detailed PDF presentation. Ideal for those seeking to enhance their understanding of GNN and its applications in machine learning and data analysis.