Enroll Course: https://www.coursera.org/learn/machine-learning-techniques
If you already have a grasp on the basics of machine learning and want to push the boundaries of your understanding, the “機器學習技法 (Machine Learning Techniques)” course on Coursera is an exceptional choice. This course serves as an extension of the foundational tools introduced in the earlier course, “Machine Learning Foundations.” The curriculum dives deeper into advanced modeling strategies by focusing on embedding numerous features, combining predictive features, and distilling hidden features.
The course structure is comprehensive, spanning 16 lectures, each bringing a wealth of knowledge on different machine learning techniques.
### Highlights of the Syllabus:
1. **Linear Support Vector Machine**: Learn robust techniques for linear classification solved via quadratic programming.
2. **Dual Support Vector Machine**: Understand QP forms of SVM that reveal valuable geometric insights.
3. **Kernel Support Vector Machine**: This lecture introduces kernels as an effective means to achieve a range of models, from simple linear to complex infinite-dimensional spaces.
4. **Soft-Margin Support Vector Machine**: Grasp the concepts of penalized margin violations and dual formulations.
5. **Kernel Logistic Regression**: Experience a two-level learning model that emphasizes soft classification.
6. **Support Vector Regression**: Dive into kernel ridge regression and explore a new approach to measuring errors.
7. **Blending and Bagging**: Learn techniques for aggregating diverse hypotheses from bootstrapped data.
8. **Adaptive Boosting**: Discover methods for optimal re-weighting and adaptive linear aggregation.
9. **Decision Tree & Random Forest**: Learn about recursive branching and ensemble methods that use randomized decision trees.
10. **Gradient Boosted Decision Tree**: Understand aggregation techniques from functional error measurements.
11. **Neural Networks & Deep Learning**: Explore automatic feature extraction using back-propagation and deep learning models.
12. **Radial Basis Function Network**: Learn about clustering and distance-based similarities in this model.
13. **Matrix Factorization**: This section details models optimized for recommender systems using user features.
14. **Finale**: Wrap up with a robust summary on practical use cases around feature exploitation, error optimization, and eliminating overfitting.
### Recommendation:
I highly recommend this course for anyone looking to solidify and expand their skill set in machine learning. The detailed exploration of various algorithmic techniques equips students with the necessary tools to tackle real-world problems effectively. The course is suitable for both intermediate learners and advanced practitioners who want to sharpen their analytical capabilities in machine learning.
In addition to the high-quality content, Coursera offers a flexible learning environment, allowing you to progress through the materials at your own pace. If advanced machine learning is your goal, this course is definitely worth the investment!
Enroll Course: https://www.coursera.org/learn/machine-learning-techniques