Machine Learning

機器學習

[ML-1] Quick Start

優先完成 [ML-1A] 與 [ML-1B] 兩個課程模組，快速建立對「機器學習流程」與「監督式學習」的核心知識與操作經驗。接者透過修習 [ML-1C]，強化資料前處理與特徵工程技巧。

[ML-2] Concepts and Workflows

在 [ML-2A] 課程模組中，採用「閱讀 → 實作 → 閱讀 → 實作」的迭代方式學習，以逐步掌握機器學習開發流程中的各個環節細節，並理解演算法的原理與應用實例。搭配 [ML-2-Ref-1] 可從統計學習的觀點，補充對機器學習概念與實作的另一角度認識。[ML-2-ShortVideos-1] 提供圖像化的簡明闡述，可快速建立對機器學習演算法的架構性理解。

[ML-3] Capstone Project

在 [ML-3A] 課程模組中，將進行自主學習影像組學 (Radiomics) 相關知識，並實作一個完整的專案，內容包含從醫學影像中萃取組學特徵，應用機器學習方法完成分類任務，並依據此流程撰寫一篇短篇論文，完整呈現研究動機、方法、結果與討論。

[ML-1] Quick Start

[ML-1] 學習建議：優先完成 [ML-1A] 與 [ML-1B] 兩個課程模組，快速建立對「機器學習流程」與「監督式學習」的核心知識與操作經驗。接者透過修習 [ML-1C]，強化資料前處理與特徵工程技巧。

[ML-1A]
End-to-End Machine Learning・DataCamp・4 Hours Course (Intermediate)

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

[ML-1B]
Supervised Learning with scikit-learn・DataCamp・4 Hours Course (Intermediate)

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

[ML-1C]
Preprocessing for Machine Learning in Python・DataCamp・4 Hours Course (Intermediate)

Learn how to clean and prepare your data for machine learning!

[ML-2] Concepts and Workflows

[ML-2] 學習建議：在 [ML-2A] 課程模組中，採用「閱讀 → 實作 → 閱讀 → 實作」的迭代方式學習，以逐步掌握機器學習開發流程中的各個環節細節，並理解演算法的原理與應用實例。搭配 [ML-2-Textbook-1] 可從統計學習的觀點，補充對機器學習概念與實作的另一角度認識。[ML-2-ShortVideos-1] 提供圖像化的簡明闡述，可快速建立對機器學習演算法的架構性理解。

[ML-2A]
Scikit-learn 詳解與企業應用：機器學習最佳入門與實戰・自學課程・4 Weeks Course (Beginner)

從機器學習的開發流程出發，透過 scikit-learn、NumPy、Pandas、Matplotlib 等工具，系統性學習機器學習演算法的原理與實務應用。

[ML-2-Ref-1]
An Introduction to Statistical Learning with Applications in Python ・ James, Witten, Hastie, Tibshirani, Taylor ・ 4 Months Course (Intermediate)

搭配 [ML-2A] 學習。The intention behind the book is to concentrate more on the applications of the methods and less on the mathematical details, so it is appropriate for advanced undergraduates or master’s students in statistics or related quantitative fields, or for individuals in other disciplines who wish to use statistical learning tools to analyze their data. It can be used as a textbook for a course spanning two semesters.

[ML-2-ShortVideos-1]
Machine Learning ・ StatQuest ・ [Playlist]

搭配 [ML-2A] 學習。For a clear and engaging introduction to machine learning, Josh Starmer’s The StatQuest Illustrated Guide To Machine Learning (2022) and his StatQuest YouTube channel break down complex concepts into bite-sized, easy-to-understand explanations. The book uses intuitive illustrations to simplify algorithms, while the short videos offer accessible, high-level overviews. Together, they are excellent resources for mastering the fundamentals of machine learning.

[ML-3] Capstone Project

[ML-3] 學習建議：在 [ML-3A] 課程模組中，將進行自主學習影像組學 (Radiomics) 相關知識，並實作一個完整的專案，內容包含從醫學影像中萃取組學特徵，應用機器學習方法完成分類任務，並依據此流程撰寫一篇短篇論文，完整呈現研究動機、方法、結果與討論。

[ML-3A]
TBD. Review the content on the Radiomics webpage and design a medical image classification project using radiomic features. Upon completion, prepare a concise report summarizing your methodology, results, and key insights from the project.

ML Learnling Resources

Online Course

Machine Learning for Everybody・feeCodeCamp.org・[03:53:52]
Machine Learning with Python and Scikit-Learn – Full Course with Notes and Codes・feeCodeCamp.org・[18:00:34]
Machine Learning・Hung-yi Lee・2021 Edition
機器學習 2022・李宏毅教授
機器學習 2021・李宏毅教授

More Machine Learning

Extreme Gradient Boosting with XGBoost・DataCamp・4 Hours Course (Intermediate)

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Machine Learning with Tree-Based Models in Python・DataCamp・5 Hours Course (Intermediate)

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Linear Classifiers in Python・DataCamp・4 Hours Course (Intermediate)

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Feature Engineering for Machine Learning in Python・DataCamp・4 Hours Course (Intermediate)

Create new features to improve the performance of your Machine Learning models.

Unsupervised Learning in Python・DataCamp・4 Hours Course (Intermediate)

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Cluster Analysis in Python・DataCamp・4 Hours Course (Intermediate)

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Time Series

Machine Learning for Time Series Data in Python・DataCamp・4 Hours Course (Intermediate)

This course focuses on feature engineering and machine learning for time series data.

To Be Organized

Introduction to Predictive Analytics in Python・DataCamp・4 Hours Course (Beginner)

In this course you'll learn to use and present logistic regression models for making predictions.

Intermediate Predictive Analytics in Python・DataCamp・4 Hours Course (Intermediate)

Learn how to prepare and organize your data for predictive analytics.

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