Machine Learning with Python & Statistics
By External Provider
About this course
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 probability models and distributions. Learners will also gain hands-on exposure to linear algebra essentials, including matrix operations and determinants, before progressing to hypothesis testing, t-tests, Chi-square analysis, goodness of fit, and covariance interpretation. What makes this course unique is its integration of mathematics, statistics, and Python implementation, ensuring learners not only understand the theory but also apply and evaluate it in practical machine learning workflows. Whether you’re preparing for advanced data science roles or strengthening your analytical foundation, this course provides the essential toolkit to succeed.
What you will learn
- Apply probability, sampling, and distributions to datasets.
- Use linear algebra and hypothesis testing for data analysis.
- Build and validate ML models with Python in real-world contexts.