🧠 IBM Machine Learning with Python

This repository contains structured notes, quizzes, labs, and summaries from the IBM Machine Learning with Python course offered via Coursera, part of the IBM AI Engineering Professional Certificate. It is intended as a reference and revision resource for quick learning and review.

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📚 Course Overview

The course provides a solid foundation in supervised and unsupervised machine learning techniques using Python. It covers core topics including regression, classification, clustering, and dimensionality reduction, using libraries like Scikit-learn, Pandas, NumPy, and Matplotlib.

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📦 Module Structure

✅ MODULE 1: Introduction to Machine Learning

• 📘 Overview of ML types and workflows
• 🧠 Difference between Data Scientist & AI Engineer
• 🔧 Tools: Scikit-learn, NumPy
• 📝 Practice Quiz: Introduction to ML
• 📝 Graded Quiz: Introduction to ML

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✅ MODULE 2: Supervised Learning Models

• 🔍 Regression & Classification Fundamentals
• 🌳 Decision Trees, SVM, KNN
• 🎓 Model Evaluation Metrics
• 📝 Practice Quiz: Building Supervised Learning Models
• 📝 Graded Quiz: Supervised Learning

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✅ MODULE 3: Model Evaluation and Pipelines

• 📏 Accuracy, Precision, Recall, F1 Score
• ⚙️ Train/Test Split, Cross Validation
• 🔁 Pipelines and Model Lifecycle
• 📝 Practice & Graded Quizzes

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✅ MODULE 4: Unsupervised Learning

• 🔗 K-means, DBSCAN, HDBSCAN Clustering
• 🔽 Dimensionality Reduction: PCA, t-SNE, UMAP
• 🧪 Hands-on Labs for clustering & feature engineering
• 📝 Graded Quiz: Building Unsupervised Learning Models

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✅ Final Exam

• 🧪 Covers supervised & unsupervised models, evaluation, ML lifecycle
• ✅ Final Exam Summary

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🛠️ Technologies Used

• Python
• Jupyter Notebooks
• Scikit-learn
• Pandas, NumPy
• Matplotlib, Seaborn

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📑 Notes

• All screenshots are included in respective folders.
• Each module folder contains a README.md with quiz answers, explanations, and links to labs or videos.
• Labs are based on Coursera's embedded environments and are documented as summaries.

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🎓 Certification Progress

Course completed as part of the IBM AI Engineering Professional Certificate.
Score Achieved: ✅ 94.52% (Final Grade)

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📬 Contact

For questions or collaboration, feel free to reach out via GitHub Issues.

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