Machine Learning with Python
This course is part of multiple programs. Learn more
Instructors: Joseph Santarcangelo +1 more
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What you'll learn
Skills you'll gain
There are 6 modules in this course
Start with regression techniques like linear, multiple linear, polynomial, and logistic regression. Then move into supervised models such as decision trees, K-Nearest Neighbors, and support vector machines. You’ll also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you’ll practice model evaluation, cross-validation, regularization, and pipeline optimization. A final project on rainfall prediction and a course-wide exam will help you apply and reinforce your skills. Enroll now to start building machine learning models with confidence using Python.
Linear and Logistic Regression
Building Supervised Learning Models
Building Unsupervised Learning Models
Evaluating and Validating Machine Learning Models
Final Project and Exam
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