AI & ML Learning Journey

Welcome to my AI & ML Learning Journey repository! This is where I document my progress, share projects, and experiment with concepts in Artificial Intelligence and Machine Learning.

🌟 Goals

• Master the fundamentals of AI & ML.
• Build practical projects demonstrating real-world applications.
• Contribute to the open-source community by sharing insights and code.

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🚀 Projects & Contributions

Implemented Models:

1. Simple Linear Regression
   • Developed a foundational regression model to understand basic linear relationships.
2. Multiple Linear Regression
   • Extended to handle multiple features for better predictions.
3. Polynomial Regression
   • Enhanced the model to capture non-linear patterns in data.
4. Support Vector Regression (SVR)
   • Integrated SVR to handle complex and non-linear relationships.
5. Decision Tree Regression
   • Implemented decision tree-based regression for interpretable and rule-based predictions.
6. Random Forest Regression
   • Leveraged ensemble techniques for improved accuracy and robustness.
7. Logistic Regression (Classification)
   • Introduced a classification model to handle binary and multi-class classification tasks.
8. K-Nearest Neighbors (K-NN) Classification
   • Built a simple yet effective classifier based on proximity to labeled data points.
9. Support Vector Machine (SVM) Classification
   • Implemented SVM to find the optimal hyperplane for classification.
10. Naive Bayes Classification
    • Applied probabilistic classification based on Bayes' theorem.
11. K-Means Clustering
    • Developed an unsupervised clustering model to group similar data points.
12. Hierarchical Clustering
    • Implemented a hierarchical approach to cluster analysis for a better understanding of data structures.

Data Preprocessing

• Data Cleaning & Feature Engineering
  • Covered techniques such as handling missing values, feature scaling, encoding categorical variables, and outlier detection.

Generic Template

• Regression & Data Preprocessing
  • Created a reusable and efficient generic template for regression workflows and data preprocessing, covering data cleaning, feature scaling, and encoding techniques.

Documentation

• README.md
  • Provided comprehensive documentation to guide contributors and showcase the repository’s purpose and usage.

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📧 Contact

If you have any feedback or questions, feel free to reach out:

• GitHub: mrXrobot26