Course Summary
Learn essential machine learning techniques and how to apply them in real-world scenarios in this comprehensive course.Key Learning Points
- Understand the fundamentals of machine learning and its various techniques
- Learn how to use machine learning algorithms to solve real-world problems
- Gain hands-on experience by working on real-life projects
Related Topics for further study
- Machine Learning Fundamentals
- Supervised Learning
- Unsupervised Learning
- Deep Learning
- Real-World Applications
Learning Outcomes
- Ability to apply essential machine learning techniques to solve real-life problems
- Hands-on experience with popular machine learning algorithms
- Understanding of the underlying principles of machine learning
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming in Python
- Familiarity with basic statistics concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures and quizzes
- Real-life projects and case studies
Similar Courses
- Applied Data Science with Python
- Advanced Machine Learning
Related Education Paths
- Google Cloud Machine Learning Engineer Certification
- Microsoft Certified: Azure AI Engineer Associate
- IBM Certified Data Engineer
Notable People in This Field
- Founder of deeplearning.ai
- Professor of Computer Science at Stanford University
Related Books
Description
The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]
Outline
- 第一講：Linear Support Vector Machine
- Course Introduction
- Large-Margin Separating Hyperplane
- Standard Large-Margin Problem
- Support Vector Machine
- Reasons behind Large-Margin Hyperplane
- NTU MOOC 課程問題詢問與回報機制
- 課程大綱
- 延伸閱讀
- 課程形式及評分標準
- 第二講：Dual Support Vector Machine
- Motivation of Dual SVM
- Lagrange Dual SVM
- Solving Dual SVM
- Messages behind Dual SVM
- 第三講：Kernel Support Vector Machine
- Kernel Trick
- Polynomial Kernel
- Gaussian Kernel
- Comparison of Kernels
- 第四講：Soft-Margin Support Vector Machine
- Motivation and Primal Problem
- Dual Problem
- Messages behind Soft-Margin SVM
- Model Selection
- 作業一
- 第五講：Kernel Logistic Regression
- Soft-Margin SVM as Regularized Model
- SVM versus Logistic Regression
- SVM for Soft Binary Classification
- Kernel Logistic Regression
- 第六講：Support Vector Regression
- Kernel Ridge Regression
- Support Vector Regression Primal
- Support Vector Regression Dual
- Summary of Kernel Models
- 第七講：Blending and Bagging
- Motivation of Aggregation
- Uniform Blending
- Linear and Any Blending
- Bagging (Bootstrap Aggregation)
- 第八講：Adaptive Boosting
- Motivation of Boosting
- Diversity by Re-weighting
- Adaptive Boosting Algorithm
- Adaptive Boosting in Action
- 作業二
- 第九講：Decision Tree
- Decision Tree Hypothesis
- Decision Tree Algorithm
- Decision Tree Heuristics in C&RT
- Decision Tree in Action
- 第十講：Random Forest
- Random Forest Algorithm
- Out-Of-Bag Estimate
- Feature Selection
- Random Forest in Action
- 第十一講：Gradient Boosted Decision Tree
- Adaptive Boosted Decision Tree
- Optimization View of AdaBoost
- Gradient Boosting
- Summary of Aggregation Models
- 第十二講：Neural Network
- Motivation
- Neural Network Hypothesis
- Neural Network Learning
- Optimization and Regularization
- 作業三
- 第十三講：Deep Learning
- Deep Neural Network
- Autoencoder
- Denoising Autoencoder
- Principal Component Analysis
- 第十四講：Radial Basis Function Network
- RBF Network Hypothesis
- RBF Network Learning
- k-Means Algorithm
- k-Means and RBF Network in Action
- 第十五講：Matrix Factorization
- Linear Network Hypothesis
- Basic Matrix Factorization
- Stochastic Gradient Descent
- Summary of Extraction Models
- 第十六講：Finale
- Feature Exploitation Techniques
- Error Optimization Techniques
- Overfitting Elimination Techniques
- Machine Learning in Practice
- 作業四
Summary of User Reviews
The machine learning techniques course on Coursera has received positive reviews from users. The course covers various machine learning techniques and their application in real-world scenarios. Many users have praised the course for its practical approach and hands-on assignments.Key Aspect Users Liked About This Course
Practical approach and hands-on assignmentsPros from User Reviews
- Great practical approach to learning machine learning techniques
- Hands-on assignments that help apply the concepts learned
- Instructors are knowledgeable and provide clear explanations
- The course covers a wide range of machine learning techniques
- The course is well-structured and easy to follow
Cons from User Reviews
- Some users found the course too technical and difficult to understand
- The course could benefit from more examples and case studies
- The assignments can be time-consuming and challenging
- The course requires a solid background in mathematics and programming
- The course may not be suitable for beginners in machine learning
機器學習技法 (Machine Learning Techniques)
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The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features....
