This module introduces the core concepts of machine learning and the fundamental role of NumPy in Python-based data science. Learners explore the advantages and challenges of machine learning, install and set up NumPy, and perform basic array operations. By the end, students gain a solid foundation for working with numerical data structures in Python.

This module focuses on data manipulation and visualization using Python’s scientific libraries. Learners advance their NumPy skills with indexing and Boolean operations, visualize data through Matplotlib plots, and master structured data handling with Pandas. These tools form the backbone of efficient exploratory data analysis.

This module introduces machine learning models through scikit-learn, covering both supervised and unsupervised approaches. Learners explore datasets, train classifiers, validate models with cross-validation, and evaluate performance metrics. By the end, they understand clustering, dimensionality reduction, and core ML workflows.

This module covers advanced applications of machine learning, including face recognition, text classification, and natural language processing. Learners extract features, train classifiers, tune parameters, and conduct sentiment analysis. The skills gained prepare students to apply machine learning in real-world contexts.