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Python-Essentials-For-Machine-Learning

by Jubril Davies

A repository containing essential fundamental elements of data handling in Machine Learning. This is not meant to be exhaustive but a starting point for every newcomer into the world of data science.

Topics covered include:

  • Array Creation using Numpy Arrays

  • Indexing and Slicing in Numpy Arrays

  • Computation using Numpy Arrays

  • Broadcasting operations using Numpy Arrays

  • Constructing Series & DataFrames

  • Indexing and Slicing Series & DataFrames

  • Operations on Pandas Data Structures

  • Data Cleaning - Handling Invalid Data, Missing Data, Duplicate data

  • Data Wrangling - Merging, Transformation,Reshaping

The codes in the jupyter notebooks have been structured in a easy to follow format highlighting the goal of a cell before implementation of its code.

The notebooks can be downloaded and worked on for independent study