Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Mastering Machine Learning Algorithms with Python provides a comprehensive understanding of key machine learning techniques and how to apply them using Python. The course covers essential concepts like data preprocessing, model training, evaluation, and optimization, equipping you with the skills to build and fine-tune machine learning models. The course begins with an introduction to machine learning, covering its history, terminology, and types of algorithms. You'll explore how data influences model outcomes and gain insights into common challenges in the field. Additionally, statistical techniques such as hypothesis testing and probability theory will be introduced to strengthen your model development. Next, you'll dive into Python programming, mastering data structures such as Pandas DataFrames and NumPy arrays. You’ll implement algorithms like linear regression and logistic regression, alongside practical projects like predicting car prices and classifying telecom churn. This course is ideal for learners with basic programming knowledge and an interest in machine learning. It’s recommended to have familiarity with Python and statistics. No prior machine learning experience is required.
What you'll learn
understand machine learning concepts
apply Python for machine learning tasks
implement algorithms like linear regression and logistic regression
perform data preprocessing and model optimization
gain insights into statistical techniques like hypothesis testing and probability theory
Course objectives
equip learners with practical skills in machine learning and Python
introduce essential machine learning techniques
provide hands-on experience with real-world projects