機器學習技法 (Machine Learning Techniques) from Coursera

What's inside

Syllabus

第一講：Linear Support Vector Machine

more robust linear classification solvable with quadratic programming

第二講：Dual Support Vector Machine

another QP form of SVM with valuable geometric messages and almost no dependence on the dimension of transformation

第三講：Kernel Support Vector Machine

kernel as a shortcut to (transform + inner product): allowing a spectrum of models ranging from simple linear ones to infinite dimensional ones with margin control

第四講：Soft-Margin Support Vector Machine

a new primal formulation that allows some penalized margin violations, which is equivalent to a dual formulation with upper-bounded variables

第五講：Kernel Logistic Regression

soft-classification by an SVM-like sparse model using two-level learning, or by a "kernelized" logistic regression model using representer theorem

第六講：Support Vector Regression

kernel ridge regression via ridge regression + representer theorem, or support vector regression via regularized tube error + Lagrange dual

第七講：Blending and Bagging

blending known diverse hypotheses uniformly, linearly, or even non-linearly; obtaining diverse hypotheses from bootstrapped data

第八講：Adaptive Boosting

"optimal" re-weighting for diverse hypotheses and adaptive linear aggregation to boost weak algorithms

第九講：Decision Tree

recursive branching (purification) for conditional aggregation of simple hypotheses

第十講：Random Forest

bootstrap aggregation of randomized decision trees with automatic validation

第十一講：Gradient Boosted Decision Tree

aggregating trees from functional + steepest gradient descent subject to any error measure

第十二講：Neural Network

automatic feature extraction from layers of neurons with the back-propagation technique for stochastic gradient descent

第十三講：Deep Learning

an early and simple deep learning model that pre-trains with denoising autoencoder and fine-tunes with back-propagation

第十四講：Radial Basis Function Network

linear aggregation of distance-based similarities to prototypes found by clustering

第十五講：Matrix Factorization

linear models of items on extracted user features (or vice versa) jointly optimized with stochastic gradient descent for recommender systems

第十六講：Finale

summary from the angles of feature exploitation, error optimization, and overfitting elimination towards practical use cases of machine learning

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

探討產業標準特徵嵌入、預測性特徵組合、與隱藏式特徵抽取。

由林軒田老師授課，在機器學習領域備受推崇。

課程內容涵蓋線性支持向量機、核支持向量機、軟間距支持向量機等常見演算法。

提供 Blending、Bagging、Adaptive Boosting 等進階技術的介紹。

探討決策樹、隨機森林、梯度提升決策樹等樹狀模型與應用。

涵蓋神經網路、深度學習、徑向基底函數網路等深度學習模型。

Reviews summary

Highly engaging ml course

機器學習技法 (Machine Learning Techniques) has received top marks from all reviewers. This course stands out for its detailed, organized, and well-presented content. Students love the interactive elements, such as programming assignments, and walk away with a deep understanding of the theoretical and practical aspects of machine learning.

Course is free to enroll.

"華语世界有这样的一门免费课程，是华语世界之幸"

Course is from NTU.

"Excellent course from NTU!"

Programming assignments reinforce learning.

"Assignments also helps me consolidate what we learned in class and I really love programming part!"

Content is well-structured and engaging.

"Teach most fundamental and popular ML algorithms from mathematics to concepts."

"The slide is awesome and the course is well-struct."

The course is excellent.

"The course is really excellent."

"A really hard course! True learners may replay experiences of great minds that have found the greatest formulas in Machine Learning."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 機器學習技法 (Machine Learning Techniques) with these activities:

阅读《机器学习实战》

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通过阅读经典著作《机器学习实战》，奠定坚实的机器学习基础，了解各种机器学习算法和技术。

View Machine Learning with Python dialysis combat... on Amazon

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阅读第 1-5 章，了解机器学习的基本概念、数据预处理和模型评估。
阅读第 6-10 章，深入学习支持向量机、决策树和集成学习算法。
尝试书中的练习和项目，以巩固你的理解并应用你的知识。

整理课程笔记和练习题

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通过整理课程笔记和练习题，加深对所学知识的理解，为复习和考试做好准备。

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回顾讲义，总结关键概念、公式和算法。
整理练习题，分类并按难度排序。
定期复习笔记和练习题，巩固你的理解。

探索支持向量机（SVM）的在线教程

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通过循序渐进的教程，深入了解支持向量机（SVM）的工作原理、不同核函数的影响以及在实际问题中的应用。

Browse courses on SVM

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完成 Coursera 上关于 SVM 的免费课程或教程。
在 YouTube 和其他在线平台上观看关于 SVM 的视频教程。
尝试使用 Scikit-Learn 等机器学习库实现 SVM 模型。

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SVM 分类练习

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通过解决一系列 SVM 分类练习题，巩固对 SVM 概念的理解，提高解决分类问题的实践能力。

Browse courses on SVM

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使用 Kaggle 或 LeetCode 等平台上的数据集构建 SVM 分类模型。
尝试不同的内核函数（如线性核、多项式核）并分析其对分类性能的影响。
对模型进行调参，以优化超参数并提高分类精度。

Career center

Learners who complete 機器學習技法 (Machine Learning Techniques) will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for building and deploying machine learning models. They work with Data Scientists to identify the right models to use, and then they develop and test the models to ensure they are accurate and efficient. This course will provide a solid foundation in machine learning algorithms and techniques, which are essential for success in this role.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are responsible for collecting, cleaning, and analyzing data. They use statistical models to identify trends, relationships, and insights from data. This course will help build a foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They use statistical models to identify trends and relationships in data. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in statistics.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends, relationships, and insights. They use statistical models and machine learning techniques to build dashboards and reports that help businesses make better decisions. This course will provide a strong foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts are responsible for developing and using mathematical and statistical models to analyze financial data. They use these models to make investment decisions and to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Quantitative Analyst

Insurance Analyst

Insurance Analysts are responsible for analyzing insurance data to identify trends and develop pricing models. They work with insurance companies to develop and implement strategies to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Insurance Analyst

Software Engineer

Software Engineers are responsible for designing, developing, and testing software applications. They work with teams of other engineers to build and maintain software systems. This course may be useful for Software Engineers who want to learn more about machine learning and data analysis techniques, which are increasingly being used in software development.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines. They work with data scientists and analysts to ensure that data is available and reliable. This course may be useful for Data Engineers who want to learn more about machine learning and data analysis techniques, which are increasingly being used in data engineering.

See salaries and explore the career path for Data Engineer

Actuary

Actuaries are responsible for assessing and managing financial risk. They use mathematical and statistical models to develop and implement strategies to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in actuarial science.

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Financial Analyst

Financial Analysts are responsible for analyzing financial data to identify trends and develop investment recommendations. They work with businesses and individuals to develop and implement investment strategies. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in financial analysis.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts are responsible for analyzing marketing data to identify trends and develop marketing strategies. They work with marketing teams to develop and implement strategies to improve marketing effectiveness. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in marketing analysis.

See salaries and explore the career path for Marketing Analyst

Risk Analyst

Risk Analysts are responsible for identifying, assessing, and mitigating risks. They work with businesses to develop and implement strategies to manage risk. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in risk management.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

Operations Research Analysts are responsible for developing and using mathematical and statistical models to solve business problems. They work with businesses to identify and solve problems in areas such as logistics, supply chain management, and manufacturing. This course will help build a foundation in machine learning and data analysis techniques, which are increasingly being used in operations research.

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Database Administrator

Database Administrators are responsible for designing, building, and maintaining databases. They work with data scientists and analysts to ensure that data is stored and managed efficiently. This course may be useful for Database Administrators who want to learn more about machine learning and data analysis techniques, which are increasingly being used in database management.

See salaries and explore the career path for Database Administrator

Business Analyst

Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They work with stakeholders to develop and implement solutions that improve efficiency and effectiveness. This course may be useful for Business Analysts who want to learn more about machine learning and data analysis techniques, which are increasingly being used in business analysis.

See salaries and explore the career path for Business Analyst