Machine learning is no longer the future—it’s the present. This course introduces the fundamentals of machine learning through real, practical projects using Python and scikit-learn.
We begin with the theory: what is machine learning, and how does it differ from traditional programming? Learners explore supervised vs. unsupervised learning, model training, testing, and evaluation metrics. Concepts like linear regression, classification, and clustering are covered with clear examples.
What sets this course apart is its hands-on focus. Students will build models to predict housing prices, detect spam, and cluster customer behavior. They’ll learn to preprocess data, handle missing values, and fine-tune algorithms for better accuracy.
The course also includes a basic introduction to neural networks and discusses ethical considerations in AI development. It’s a great starting point for aspiring data scientists, AI engineers, or professionals who want to leverage machine learning in their industries.
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