Machine Learning

Welcome to my Machine Learning repository!

This repository documents my journey of learning Machine Learning using Python from the fundamentals to advanced algorithms.

The goal is not only to understand the theory behind each algorithm but also to implement them using real-world datasets and visualize the results.

---

Topics Covered

• Linear Regression
• Ridge Lasso ElasticNet Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Naive Bayes
• Decision Trees
• Random Forest
• Unsupervised Learning
• Model Evaluation
• Feature Engineering

---

Libraries Used

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Statsmodels

---

Repository Structure

Machine-Learning
│
├── 01_Linear_Regression
├── 02_Ridge_Lasso_ElasticNet_Regression
├── 03_Logistic_Regression
├── 04_KNN
├── 05_SVM
├── 06_Naive_Bayes
├── 07_Decision_Trees
├── 08_Random_Forest
├── 09_Unsupervised_Learning
├── Images
└── README.md

---

Objective

This repository is intended to:

• Learn Machine Learning from scratch.
• Understand the mathematics behind algorithms.
• Implement algorithms using Python.
• Perform data preprocessing and visualization.
• Build a strong portfolio for internships and future projects.

---

Author

Anant Vyas

Mechanical Engineering Undergraduate

Interested in:

• Machine Learning
• Data Analytics
• Python
• ANSYS
• Mechanical Engineering Applications