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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

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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