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