From d9d9cab167b15a1231e1f191be274fbab035a71a Mon Sep 17 00:00:00 2001 From: ozsari Date: Sat, 30 Nov 2024 09:22:48 +0000 Subject: [PATCH] add notebook links --- README.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f39f51a..b9dbcef 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ # Introduction to Machine Learning: One-Day Course This is a one-day machine learning introductory course for beginners. The course covers the basics of supervised and unsupervised learning, including regression, classification, clustering, dimensinality reduction and anomaly detection. It also includes hands-on exercises and examples using popular Machine Learning (ML) libraries like Scikit-learn. -The [slides](presentation/ML_intro.pdf) are used to guide the instructor through the course, providing a structured outline of the topics to be covered. +The [presentation](presentation/ML_intro.pdf) is used to guide the instructor through the course, providing a structured outline of the topics to be covered. ## Table of Contents 1. [Introduction to Machine Learning](#1-introduction-to-machine-learning) @@ -33,6 +33,7 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - Comparison between supervised and unsupervised learning using Linear Regression and K-Means examples. - Basic visualizations of regression and clustering tasks. +**Related notebook:** [Introduction to Machine Learning](notebooks/1-Introduction_to_Machine_Learning.ipynb) --- @@ -55,6 +56,8 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - End-to-end example of an ML pipeline using Scikit-learn. - Visualization of preprocessing and evaluation results. +**Related notebook:** [Machine Learning Workflow](notebooks/2-Understanding_ML_Workflow.ipynb) + --- ## 3. Supervised Learning @@ -71,6 +74,8 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - Hands-on example of Linear Regression with visualization of results. - Analysis of regression coefficients. +**Related notebook:** [Supervised Learning - Regression](notebooks/3-Supervised-1-Regression.ipynb) + --- ### 3.2 Classification @@ -86,6 +91,8 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - Hands-on exercise with Random Forest Classifier. - Visualization of confusion matrix results. +**Related notebook:** [Supervised Learning - Classification](notebooks/3-Supervised-2-Classification.ipynb) + --- ## 4. Unsupervised Learning @@ -104,6 +111,8 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - K-Means Clustering example with synthetic data. - Visualizing clusters and centroids. +**Related notebook:** [Unsupervised Learning - Clustering](notebooks/4-Unsupervised-1-Clustering.ipynb) + --- ### 4.2 Other Unsupervised Learning Techniques @@ -122,6 +131,8 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - Hands-on example of Isolation Forest for anomaly detection. - Apriori algorithm for discovering association rules. +**Related notebook:** [Unsupervised Learning - Other Techniques](notebooks/4-Unsupervised-2-Others.ipynb) + --- ## 5. In-Class Assignment @@ -134,6 +145,8 @@ The [slides](presentation/ML_intro.pdf) are used to guide the instructor through - Train, evaluate and optimize the model. - Submit the pickle file of the trained model. + **Related notebook:** [In-Class Assignment](notebooks/5-In-Class-assignment.ipynb) + --- ### Usage