Skip to content

Covered All Machine Learning Domain Knowledge Hands on practices, Worked on Data Preprocessing steps that include Data Cleaning, EDA, Featue Engineering Train on Different type of Models for Classification or Regressiong problems and many more.

Notifications You must be signed in to change notification settings

faridelya/Machine-Learning

Repository files navigation

Machine-Learning

Covered all parts of Machine Learning Domain Knowledge

The Following folders contain mutilpe notebooks for each practical implementation

  1. -Working with CSV
  2. -Working with JSON-SQL
  3. -Working with API
  4. -WebScrapying with Beautiful-Soup
  5. -Classification Metrics
  6. -Feature Engineering

> Feature Engineering

Feature Engineering is the process of using domain knowledge to extract feature from raw data. these feature can be used to improve performance of Machine Learning Algorithms. This consist of the following techniques.

  • Feature Transformation

  • Missing Values

  • Handling Categorical Features

  • Outlier Detection

  • Feature Scaling

  • Feature Construction

Feature Selection

Feature Extraction

About

Covered All Machine Learning Domain Knowledge Hands on practices, Worked on Data Preprocessing steps that include Data Cleaning, EDA, Featue Engineering Train on Different type of Models for Classification or Regressiong problems and many more.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published