Description
This hands-on course teaches the fundamentals of machine learning using Python. It begins with basic data preparation, feature selection, and model building using libraries like scikit-learn. Key algorithms covered include linear regression, decision trees, support vector machines, and k-nearest neighbors. Students learn how to train, evaluate, and optimize models through cross-validation and performance metrics. Real-world datasets are used in projects such as spam detection, sales prediction, and medical diagnoses. The course also covers supervised vs. unsupervised learning and introduces students to pipelines and deployment techniques. Ideal for data analysts, engineers, and developers seeking to enter the machine learning field with a practical, Python-based approach.
松尾 真由美
I finally understand this topic thanks to the way it was taught.
Michael Evans
講師の説明が丁寧で理解しやすかったです。
田中 遼
初心者にもやさしい説明で、助かりました。
Isabella Scott
A well-structured course that covers all the important topics.