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🤖 ML-Practice

This repository contains my personal practice, notes, and projects while studying Machine Learning through the Machine Learning Specialization on Coursera, taught by Professor Andrew Ng from Stanford University.


🏆 Course Completed

Machine Learning Specialization

  • 👨‍🏫 Instructor: Andrew Ng
  • 🏫 Platform: Coursera
  • 🎓 Offered by: Stanford University & DeepLearning.AI
  • ✅ Status: Completed

📚 Topics Covered

The specialization contains 3 courses:

# Course Topics
1 Supervised Machine Learning Linear Regression, Logistic Regression, Gradient Descent
2 Advanced Learning Algorithms Neural Networks, Decision Trees, Random Forest, XGBoost
3 Unsupervised Learning Clustering, Anomaly Detection, Recommender Systems

📁 Repository Structure

ML-Practice/
├── Course_1_Supervised_Learning/
│   ├── 01_Linear_Regression.ipynb
│   ├── 02_Logistic_Regression.ipynb
│   └── 03_Gradient_Descent.ipynb
├── Course_2_Advanced_Algorithms/
│   ├── 04_Neural_Networks.ipynb
│   ├── 05_Decision_Trees.ipynb
│   └── 06_XGBoost.ipynb
├── Course_3_Unsupervised_Learning/
│   ├── 07_K_Means_Clustering.ipynb
│   ├── 08_Anomaly_Detection.ipynb
│   └── 09_Recommender_Systems.ipynb
└── README.md

🧠 What I learned

Course 1 — Supervised Machine Learning

  • How Linear Regression predicts continuous values
  • How Logistic Regression solves classification problems
  • How Gradient Descent optimizes a model
  • How to evaluate a model using cost function

Course 2 — Advanced Learning Algorithms

  • How Neural Networks learn using forward and backward propagation
  • How Decision Trees split data to make predictions
  • How ensemble methods like Random Forest and XGBoost improve accuracy
  • How to prevent overfitting using regularization

Course 3 — Unsupervised Learning

  • How K-Means Clustering groups unlabeled data
  • How Anomaly Detection finds unusual patterns
  • How Recommender Systems suggest content to users

🛠️ Tools and Libraries used

Python NumPy Matplotlib Scikit-Learn Jupyter TensorFlow


💡 Key concepts I can apply

  • Build and train a machine learning model from scratch
  • Choose the right algorithm for a given problem
  • Evaluate and improve model performance
  • Apply ML to real world problems like price prediction, image classification, and fraud detection

🔮 What I want to build next

  • House price prediction model using Linear Regression
  • Student pass or fail prediction using Logistic Regression
  • Image classifier using Neural Networks
  • Fraud detection system using Anomaly Detection

📖 Resources


🎓 Made by Rayhan Uddin · Computer Science Student · Bangladesh

"The best way to learn machine learning is to build things with it."

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