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.

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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

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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.

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Tools and Frameworks

• Python: Primary language for implementation
• Libraries: NumPy, Pandas, scikit-learn, Matplotlib, Seaborn
• Development Tools: Jupyter Notebook, VS Code

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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.

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Getting Started

1. Clone this repository:

   git clone https://github.com/your-username/Machine-Learning-2.0.git

2. Navigate to the project directory:

   cd Machine-Learning-2.0

3. Install dependencies:

   pip install -r requirements.txt

4. Start learning or contributing!

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Progress Tracking

I will keep the repository updated with weekly progress. Stay tuned for exciting projects, challenges, and solutions!

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Feel free to connect with me for collaboration or feedback. Let's dive deep into the fascinating world of Machine Learning together!