This course covers Support Vector Machines (SVM) from basic to advanced kernel-based models. It is designed for those who want to apply machine learning to real-world business problems and includes topics like hyperparameter tuning and model performance evaluation.
Structured path covering Python, ML fundamentals, and deep learning basics for aspiring AI practitioners.
Bridge the gap between business and AI engineering. Learn to define AI product requirements, prioritize ML features, communicate with data science teams, and launch successful AI-powered products.
Your first steps into machine learning. Understand supervised and unsupervised learning, train your first models, and build intuition for how algorithms learn from data.
Build on your existing knowledge with intermediate machine learning techniques and real-world applications.
Master advanced machine learning concepts with expert-level content and cutting-edge techniques.
Start your journey into reinforcement learning with foundational concepts and hands-on exercises designed for newcomers.
Master advanced reinforcement learning concepts with expert-level content and cutting-edge techniques.
Log in to write a review
Loading reviews...
Explore more courses and learning paths related to Support Vector Machines in Python: SVM Concepts & Code.
Browse more courses from Udemy
See the side-by-side breakdown and our pick by scenario
See the side-by-side breakdown and our pick by scenario
More advanced-level AI and ML courses
Follow the full AI/ML Fundamentals Track learning path
Browse 350+ structured AI learning paths from beginner to advanced