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Machine_Learning_Fundamentals

This repo contains a series of guided jupyter notebooks focusing on essential ML concepts.

Notebooks Included

  1. Linear Regression
  2. Data Preprocessing
  3. Data Types and Attributes
  4. Binary Classification
  5. Clustering
  6. K-Nearest Neighbors (KNN)
  7. Naive Bayes
  8. Comprehensive EDA performed on "Data Science Jobs and Salary" dataset
  9. Comprehensive EDA performed on "Loan Approval Prediction" dataset
  10. Models implementation performed on "Loan Approval Prediction" dataset

Features

  • Comprehensive
  • Practical Examples
  • Easy to Understand

Environment

  • Python
  • Jupyter Notebook

Usage

  • Learning: Use the notebooks to deepen your understanding of various ML concepts.
  • Teaching: Share these notebooks with students to facilitate learning in classrooms or workshops.

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