👋 Introduction

Welcome to the Machine Learning Course!

This course introduces the foundational concepts and techniques of machine learning, focusing on supervised learning methods like linear regression, logistic regression, and ensemble models, along with a brief exploration of unsupervised learning. You’ll learn how to prepare data, build predictive models, and evaluate their performance—all while applying these skills to real-world problems in domains like business, healthcare, and education.

📋 Course Overview

🗝️ Key Topics Covered

Foundations of Machine Learning

What is Machine Learning?
Supervised vs. Unsupervised Learning
The ML workflow: data → model → evaluation

Supervised Learning

Linear Regression: predicting continuous outcomes
Logistic Regression: binary classification
Model Evaluation: accuracy, precision, recall, F1-score
Overfitting & Regularization: bias-variance tradeoff

Ensemble Learning

Decision Trees: intuitive models for classification
Random Forests: bagging and feature importance
Gradient Boosting: boosting weak learners
Comparison of ensemble methods

Unsupervised Learning (Intro)

Clustering: k-Means and Hierarchical Clustering
Dimensionality Reduction: PCA basics
Applications in customer segmentation and pattern discovery

By the end of this course, you’ll be able to build and evaluate basic machine learning models, understand their strengths and limitations, and apply them to structured datasets with confidence.

📝 Course Criteria

Criteria	Percentage
Attendance	10%
Participation & quiz	30%
Midterm Exam	30%
Final Project & Presentation / Practical labs	30%

💻 Programming:

You are free you use your favorite programming language Python or .

🗺️ Course progress

Note: The following table of contents will be progressively updated according to the course advancement.

Topic	Lab	Solution	Remark
…	…	…	…
Linear Regression	Lab1	Solution1	…Loading
Logistic Regression	Lab2	Solution2	…Loading
k-nearest neighbors (k-NN)	Lab3	Solution3	…Loading
Decision Trees	Lab4	Solution4	…Loading
Ensemble Learning	Lab5	Solution5	…Loading

📄 Midterms, Exams and Projects

In this section, you will find all the information related to the midterms, exams and projects including instructions, starting dates and the deadlines.

📄 Midterm & Exam

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📄 Project:

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Where to submit: Canvas
Your report should be in (your favorite) PDF format and include the following criteria:

Introduction

Objective: Define the ML problem (e.g., classification, regression, clustering).
Dataset: Describe the source, size, and features (e.g., Kaggle, UCI).
Relevance: Why is this problem important in a machine learning context?

Data Preprocessing

Cleaning: Handle missing values, outliers, duplicates.
Transformation: Scaling, encoding, feature engineering.
Selection: Feature importance, PCA, correlation analysis.

Exploratory Data Analysis (EDA)

Statistics & Visuals: Summary stats, distributions, pair plots.
Insights: Identify trends, anomalies, class imbalance.

Model Development

Algorithms: Justify choices (e.g., Logistic Regression, Random Forest, SVM).
Training Strategy: Train/test split, cross-validation.
Tuning: Grid search, random search, Bayesian optimization.

Evaluation Metrics:

Classification: Accuracy, Precision, Recall, F1, ROC-AUC.
Regression: RMSE, MAE, R².
Clustering: Silhouette Score, Davies-Bouldin Index.
Visuals: Confusion matrix, learning curves, residual plots.

Discussion & Challenges

Limitations: Data quality, overfitting, interpretability.
Implications: How results apply to real-world scenarios.

Conclusion & Future Work

Summary: Key takeaways and model performance.
Next Steps: Improvements, alternative models, deployment ideas.

References

Cite datasets, libraries (e.g., scikit-learn, TensorFlow), and relevant papers.

Appendix (Optional)

Code snippets, extended results, additional plots.

Presentation:

A possible dates: ...Loading.

📚 Resources and Further Reading

Explore these resources to deepen your understanding of Machine Learning: