https://github.com/mdalamin5/machine-learning-2.0
Machine-Learning-2.0: A comprehensive repository documenting my journey to master ML from scratch. It includes core algorithms, advanced techniques, data preprocessing, feature engineering, and real-world projects. Follow my structured approach, inspired by "100 Days of ML," featuring Python implementations, tools, and insightful resources.
https://github.com/mdalamin5/machine-learning-2.0
data-fetching-from-api datapreprocessing end-to-end-project feature-engineering gradient-descent-optimizers machine-learning-algorithms scikit-learn webscraping-data
Last synced: 3 months ago
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Machine-Learning-2.0: A comprehensive repository documenting my journey to master ML from scratch. It includes core algorithms, advanced techniques, data preprocessing, feature engineering, and real-world projects. Follow my structured approach, inspired by "100 Days of ML," featuring Python implementations, tools, and insightful resources.
- Host: GitHub
- URL: https://github.com/mdalamin5/machine-learning-2.0
- Owner: MDalamin5
- License: apache-2.0
- Created: 2024-12-19T11:07:21.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-13T08:46:36.000Z (3 months ago)
- Last Synced: 2025-02-13T09:33:19.881Z (3 months ago)
- Topics: data-fetching-from-api, datapreprocessing, end-to-end-project, feature-engineering, gradient-descent-optimizers, machine-learning-algorithms, scikit-learn, webscraping-data
- Language: Jupyter Notebook
- Homepage: https://www.linkedin.com/in/mdalamin5/
- Size: 23.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine-Learning-2.0
Welcome to the **Machine-Learning-2.0** repository! This project is my journey to revisit and master the core concepts, algorithms, and advanced techniques of Machine Learning (ML) from scratch. With a strong foundation built during my 3rd year, this repository will act as a detailed documentation of my restart into ML, focusing on implementation, data preprocessing, feature engineering, and cutting-edge projects.
## Objectives
- Relearn and implement all major Machine Learning algorithms.
- Perform comprehensive data preprocessing and feature engineering.
- Dive deep into advanced ML concepts and projects.
- Build a solid portfolio of ML projects to demonstrate expertise.
---
## Repository Contents
1. **Data Preprocessing**
- Handling missing data
- Data transformations
- Feature scaling and encoding
- Dealing with outliers and imbalanced datasets
2. **Feature Engineering**
- Feature construction and selection
- Dimensionality reduction techniques (e.g., PCA)
- Advanced transformations and feature interactions
3. **Core Machine Learning Algorithms**
- Supervised Learning
- Linear Regression (Simple, Multiple, Ridge, Lasso, ElasticNet)
- Logistic Regression
- Decision Trees
- Ensemble Methods (Random Forest, AdaBoost, Gradient Boosting)
- Unsupervised Learning
- Clustering (K-Means, Hierarchical Clustering)
- Anomaly Detection
- Semi-Supervised and Reinforcement Learning (as extensions)
4. **Gradient Descent Optimization**
- Batch, Stochastic, and Mini-Batch Gradient Descent
- Hyperparameter tuning techniques
5. **Advanced Topics**
- Regularization techniques
- Bias-variance tradeoff
- Overfitting and underfitting
- Cross-validation and model evaluation
6. **Projects**
- End-to-end ML pipelines
- Real-world datasets and case studies
- Advanced ML model deployment
---
## Learning Schedule
I will follow a structured approach, inspired by the **100 Days of Machine Learning** playlist by CampusX, to build a robust understanding of Machine Learning concepts. The repository will evolve as I progress through:
- Conceptual learning
- Coding implementations from scratch
- Applying learned techniques to real-world problems.
---
## Tools and Frameworks
- **Python**: Primary language for implementation
- **Libraries**: NumPy, Pandas, scikit-learn, Matplotlib, Seaborn
- **Development Tools**: Jupyter Notebook, VS Code
---
## How to Use This Repository
- **Learners**: Follow along to learn ML from the ground up.
- **Contributors**: Feel free to suggest improvements or contribute to projects.
- **Researchers**: Refer to the code and implementations for understanding ML algorithms.
---
## Getting Started
1. Clone this repository:
```bash
git clone https://github.com/your-username/Machine-Learning-2.0.git
```
2. Navigate to the project directory:
```bash
cd Machine-Learning-2.0
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Start learning or contributing!
---
## Progress Tracking
I will keep the repository updated with weekly progress. Stay tuned for exciting projects, challenges, and solutions!
---
Feel free to connect with me for collaboration or feedback. Let's dive deep into the fascinating world of Machine Learning together!