IBM Machine Learning with Python

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📘 About this Course #

This repository contains my work, labs, and projects from the IBM Machine Learning with Python course, part of the IBM AI Engineering Professional Certificate on Coursera.

The course covered the end-to-end machine learning workflow, from supervised and unsupervised learning to pipelines, evaluation, and real-world applications.

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🗂️ Modules Overview #

Module 1: Introduction to Machine Learning #

• Types of ML: supervised, unsupervised, reinforcement
• Linear Regression basics (CO₂ emissions prediction)

Module 2: Regression Models #

• Multiple Linear Regression, Polynomial & Nonlinear Regression
• Logistic Regression (binary classification)
• Labs: Regression on automotive and logistic datasets

Module 3: Classification Algorithms #

• Decision Trees & Regression Trees
• Random Forests & XGBoost (ensemble methods)
• Support Vector Machines (credit card fraud detection)
• K-Nearest Neighbors (KNN classification)

Module 4: Clustering & Dimensionality Reduction #

• K-Means clustering (customer segmentation)
• DBSCAN vs. HDBSCAN (geospatial data)
• PCA, t-SNE, UMAP (dimensionality reduction & visualization)

Module 5: Model Evaluation & Validation #

• Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
• Cross-validation & stratified CV
• Regularization (Ridge, Lasso)
• Pipelines with GridSearchCV

Module 6: Projects #

• Practice Project: Titanic survival prediction

  • Preprocessing with ColumnTransformer (imputation, scaling, encoding)
  • Models: Logistic Regression, Random Forest
  • Addressed class imbalance with class_weight="balanced"
  • Achieved ~0.82 F1-score

• Final Project: Rainfall prediction (Melbourne dataset)

  • Feature engineering (seasonal features, leakage removal)
  • Pipeline + GridSearchCV for hyperparameter tuning
  • Evaluated with ROC-AUC, precision, recall
  • Found Sunshine & Humidity3pm most predictive

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🛠️ Skills Gained #

• Machine Learning fundamentals with Python & scikit-learn
• Building and evaluating regression & classification models
• Handling imbalanced datasets (sample weights, class weighting)
• Unsupervised learning: clustering & dimensionality reduction
• Pipelines for reproducible ML workflows
• Model evaluation with cross-validation & metrics
• Feature engineering & data preprocessing

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📂 Repository Structure #

├── Module2_Regression/ ├── Module3_Classification/ ├── Module4_Clustering/ ├── Module5_Evaluation/ ├── Projects(module6)/ │ ├── Titanic/ │ ├── Rainfall/ └── README.md

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🚀 Next Steps #

This course provided a strong foundation in classical ML.
Next, I am applying these skills in Deep Learning (Keras, CNNs, RNNs, Transformers) as part of the broader IBM AI Engineering Certificate.

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✍️ Author: Ahmad Tawil
📅 Completed: September 6, 2025