This repository documents my Machine Learning (ML) learning journey.
I am currently studying ML concepts step by step and uploading:
- Lecture materials
- Practice implementations
- Concept-based examples
This repository will continue to grow as I complete more ML topics and build projects.
- Study ML theory from lectures
- Implement concepts in Python
- Practice small examples
- Gradually build real-world ML projects
- Track my learning progress over time
- Lecture notes and materials
- Basic ML concept implementations
- Initial experimentation with ML libraries
- Introduction to Machine Learning
- Types of ML (Supervised, Unsupervised)
- Dataset understanding
- Data preprocessing (basic)
- Linear Regression
- Logistic Regression
- Classification basics
- Model evaluation basics
- K-Nearest Neighbors (KNN)
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- Clustering (K-Means)
- Feature Engineering
- Model evaluation metrics
- Overfitting & Underfitting
- Cross-validation
- Neural Networks (Basic)
- Deep Learning (Future goal)
- Language: Python
- Libraries: NumPy, Pandas, Matplotlib, Scikit-learn (learning phase)
- IDE: VS Code / Jupyter Notebook
- Build strong ML fundamentals
- Develop real-world ML projects
- Work on AI-based solutions
This repository will include:
- Mini ML projects
- Dataset-based model building
- Performance comparison of algorithms
- End-to-end ML workflows
- Currently learning (Beginner to Intermediate transition)
- Updating regularly
- Expanding topic coverage gradually
This repository represents an ongoing learning process. Content will be continuously updated as I progress in Machine Learning.
β This repository reflects my journey toward becoming skilled in Machine Learning.