Machine Learning in practice with Python’s own scikit-learn on real-world datasets!
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Predict the values of continuous variables using linear regression and K Nearest Neighbors.
Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically.
Build a portfolio of tools and techniques that can readily be applied to your own projects.
Use Support Vector Machines to learn how to train your model to predict the chances of heart disease.
Analyze the population and generate results in line with ethnicity and other factors using K-Means Clustering.
This comprehensive Practical scikit-learn for Machine Learning: 4-in-1 curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
What you need before starting this Practical scikit-learn for Machine Learning: 4-in-1 course:
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Everything you need to know about this online course, from duration to certification
Course Instructor
Packt Publishing
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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