Enroll Course: https://www.coursera.org/learn/machine-learning-techniques
Machine Learning has become a cornerstone of technology and data science, and the course ‘機器學習技法’ (Machine Learning Techniques) offered on Coursera is an excellent opportunity to deepen your expertise in this ever-evolving field. Designed to build on the foundations of ‘Machine Learning Foundations’, this course extends your knowledge to include powerful and practical models through three main directions: incorporating numerous features, combining predictive features, and extracting hidden features.
### Course Overview
The course syllabus is structured into 16 comprehensive lectures, each focusing on specific techniques that are crucial for mastering machine learning. Here’s a brief overview of the key topics:
1. **Linear Support Vector Machine** – Robust linear classification utilizing quadratic programming.
2. **Dual Support Vector Machine** – A QP form of SVM that emphasizes valuable geometric insights.
3. **Kernel Support Vector Machine** – This lecture covers the kernel’s role in transforming data and enabling models of various complexities.
4. **Soft-Margin Support Vector Machine** – Focuses on a new primal formulation to handle margin violations.
5. **Kernel Logistic Regression** – A soft-classification approach through SVM-like models.
6. **Support Vector Regression** – Looks at kernel ridge regression and support vector regression techniques.
7. **Blending and Bagging** – Techniques for obtaining diverse hypotheses through data bootstrapping.
8. **Adaptive Boosting** – Discusses optimal re-weighting methods to enhance weak algorithms.
9. **Decision Tree** – Explains recursive branching for hypothesis aggregation.
10. **Random Forest** – Covers bootstrap aggregation for decision trees.
11. **Gradient Boosted Decision Tree** – Techniques for aggregating trees through functional descent.
12. **Neural Network** – Automatic feature extraction via back-propagation.
13. **Deep Learning** – Introduces early deep learning models with denoising autoencoders.
14. **Radial Basis Function Network** – Focuses on aggregating distance-based similarities.
15. **Matrix Factorization** – Joint optimization of item features for recommender systems.
16. **Finale** – Summarizes feature exploitation and error optimization for practical applications.
### Recommendation
This course is a fantastic resource for learners who already have a fundamental understanding of machine learning concepts and wish to advance their skills. The practical approach, combined with theoretical insights, makes it suitable not only for students but also for professionals seeking to enhance their data science toolkit.
The course covers both foundational and advanced topics, thus catering to a wide audience. The use of real-world applications and case studies helps solidify understanding and prepares learners for challenges in data analysis and machine learning tasks.
If you want to elevate your understanding of machine learning and put theory into practice, I highly recommend enrolling in ‘機器學習技法’. It’s a must for anyone serious about stepping up their game in machine learning.
Enroll Course: https://www.coursera.org/learn/machine-learning-techniques