Description: Foundations of Machine and Deep Learning Using Python introduces the core principles and practical implementation of intelligent algorithms using the Python programming language. It covers essential concepts such as data preprocessing, supervised and unsupervised learning, neural networks, and deep learning architectures.
The subject emphasizes the use of popular libraries like NumPy, Pandas, Scikit-learn, and TensorFlow for building and training models. Learners gain hands-on experience in implementing algorithms for classification, regression, clustering, and prediction tasks.
By combining theoretical understanding with coding practice, this foundation enables students to develop intelligent systems and apply machine and deep learning techniques to real-world problems in modern computing.