Part four goes into depth with several key libraries: NumPy, Matplotlib, Pandas, Seaborn (for data visualization), and Scikit-learn. Each library is demonstrated with practical examples.

Python library options: NumPy and Pandas. There are many powerful Python C libraries that provide high performance for scientific applications that process large amounts of data in arrays or matrices.

Programme Highlights Hands-on Python Programming: Focus on Python, Numpy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras. Real-World Projects: Apply learned ...