Machine Learning Fundamentals with Python gives you a clear, practical introduction to the core supervised and unsupervised learning techniques used in modern data science. Through hands-on examples and Python-based implementations, you’ll learn how to apply essential ML algorithms—regression, classification, clustering, and dimensionality reduction—to real-world pattern modeling and business problems.
 
What You’ll Learn
Foundations of data science & ML: Understand key concepts behind supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction).
Practical algorithm application: Learn how to select and apply ML techniques to solve regression, classification, and pattern-detection challenges.
Python implementation: Develop end-to-end machine learning workflows in Python, using libraries commonly adopted in business and IT environments.
Model interpretation: Gain familiarity with interpretable ML methods that help you understand how your models behave and why they make certain predictions.
 
Why This Course Is for You
You’re a developer who wants to understand the essential building blocks of machine learning.
You’re an IT professional looking to expand your skill set with supervised and unsupervised ML techniques.
You’re exploring data-focused roles and want a solid foundation to support a long-term career transition.
You want to strengthen your ability to analyze, model, and interpret data using Python.
 
Prerequisites
Basic Python programming (loops, functions, structures).
A beginner-level understanding of arithmetic logic.