Learn By Example: NumPy

All organizations today deal with huge datasets, getting the data cleaned and in the right numeric form so it can be used for further analysis and fed into machine learning models is an extremely important step. NumPy is a core library in the Python data science stack which allows you to work with huge multidimensional data.

Here is what is covered in this course:

Basic operations and universal functions: NumPy libraries to create and work with arrays and perform common mathematical computations

Reshaping and automatic reshaping: A common operation when using ML algorithms

Indexing, slicing and splitting arrays: A common operation when performing feature engineering with numeric data

Conditional evaluation: Extract only those elements which match your requirements

Broadcasting scalars and arrays: Allows computations on arrays of mismatched shapes

Image manipulation: Expressing images as matrices and learning basic image pre-processing techniques

Pandas, SciPy integration: NumPy works seamlessly with other libraries in the PyData stack

This course is built around hands on demos, built to explicitly explain concepts underpinning each topic.

Software used: Python 3, NumPy Python APIs