- Published on
Machine Learning with Python
From Fundamentals to Practical Applications
- Authors
Shaimaa Atraoui
This course introduces the core concepts of Machine Learning, focusing on understanding algorithms, data preparation, and model evaluation through practical Python implementations. It is designed for students with basic programming knowledge who want to build reliable and interpretable ML models.
Learning Objectives
- Understand the main paradigms of machine learning
- Prepare and preprocess real-world datasets
- Implement supervised and unsupervised algorithms in Python
- Evaluate and compare machine learning models
- Apply ML techniques to real use cases
📘 Course Syllabus
1. Introduction to Machine Learning
- Artificial Intelligence vs Machine Learning vs Deep Learning
- Types of learning: supervised, unsupervised, semi-supervised
- Typical ML workflow
2. Data Preparation & Feature Engineering
- Data cleaning and handling missing values
- Feature scaling and normalization
- Train/test split and cross-validation
3. Supervised Learning
- Linear Regression
- Logistic Regression
- k-Nearest Neighbors (k-NN)
- Decision Trees and Random Forests
4. Unsupervised Learning
- Clustering concepts
- k-Means algorithm
- Hierarchical clustering
- Dimensionality reduction (PCA – introduction)
5. Model Evaluation
- Confusion matrix
- Accuracy, precision, recall, F1-score
- Bias–variance tradeoff
- Overfitting and underfitting
6. Practical Case Studies
- End-to-end ML project
- Interpretation of results
- Discussion of limitations
Tools & Technologies
- Python
- NumPy, Pandas
- Matplotlib
- Scikit-learn
🔗 Course materials:
Slides, Jupyter notebooks, exercises, datasets
👉 Link available upon request
