Machine Learning with Python & Statistics

Learners will be able to apply probability, sampling, distributions, and statistical testing to analyze datasets and build machine learning models with Python. By the end of this course, they will differentiate data types, evaluate hypothesis testing approaches, and utilize linear algebra and inferential methods to interpret and validate results in real-world contexts. This course provides a step-by-step pathway through the foundations of machine learning, beginning with supervised and unsupervised learning concepts, advancing into sampling techniques and data classification, then exploring p…