???? Day 181 of 365: SVM Scalability and Real-World Applications ????
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Scaler!
Welcome to Day 181 of our #365DaysOfDataScience journey! ??
We'll tackle real-world challenges together, making sure we're comfortable with handling large datasets and scaling SVMs effectively! It's going to be fun to explore how different tools and techniques can make even complex models scalable.
?? What We’ll Be Exploring Today:
- Challenges of using Support Vector Machines (SVM) on large datasets.
- Libraries and techniques to scale SVM models effectively (e.g., LIBSVM, linear SVMs).
?? Learning Resources:
- Read: Articles on scaling SVMs for large datasets.
- Explore: Look into libraries like LIBSVM and other approaches to handling SVMs at scale.
?? Today’s Task:
- Experiment: Train an SVM on a large dataset and explore different scaling techniques to improve performance. Apply tools like LIBSVM or linear approximations, and compare the results with your previous models on smaller datasets.
Happy Learning & See You Soon!