Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as Amazon Alexa products reviews
Understand the theory and intuition behind several machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Build an e-mail spam classifier using Naive Bayes classification Technique
Apply machine learning models to Healthcare applications such as Cancer and Kyphosis diseases classification
This comprehensive Machine Learning Classification Bootcamp in Python 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.
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What you need before starting this Machine Learning Classification Bootcamp in Python course:
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Everything you need to know about this online course, from duration to certification
Course Instructor
Dr. Ryan Ahmed, Ph.D., MBA
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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