Machine Learning with Python

Machine Learning with Python introduces the fundamentals of machine learning and provides practical knowledge of how Python can be used to build and implement machine learning solutions. The course covers the complete machine learning workflow, from collecting and preparing data to training, evaluating, and improving models. Participants learn essential Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn for data analysis and machine learning applications. The course explains supervised and unsupervised learning concepts along with commonly used algorithms for classification, regression, and clustering. Participants gain an understanding of data preprocessing, feature selection, model training, and performance evaluation. Practical demonstrations help learners understand how machine learning models are developed using real-world datasets. The course also introduces techniques for handling missing data, scaling features, and avoiding overfitting. Model evaluation methods and performance metrics are discussed to help participants select suitable algorithms for different applications. By combining theoretical concepts with Python-based practice, the course develops practical machine learning skills applicable to engineering, manufacturing, automation, and Industry 4.0 environments.