This course is regarding Python powerful libraries:

(1)Python NumPy

(2)Python SciPy 

(3)Python Pandas

(4)Python matplotlib

(5)Python sys

(6)Python seaborn

(7)Python Scikit-learn

(8)Python TensorFlow

Each topic will be covered with practical examples and real-life scenarios.

- SciPy is the scientific equivalent of NumPy, offering tools and techniques for analysis of scientific data.

- Scilkit-Learn is the machine learning libraries that provide modules for building neural networks and data preprocessing.

- Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.

- Pandas are the Python Data Analysis Library, used for everything from importing data from Excel spreadsheets to processing sets for time-series analysis.

- Sys stands for System-specific parameters and functions. This module provides access to some variables used or maintained by the interpreter and to functions that interact strongly with the interpreter.

- Seaborn: statistical data visualisation. Seaborn is a Python visualisation library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

- Scikit-learn provides a range of supervised and unsupervised Machine learning algorithms via a consistent interface in Python.

- TensorFlow is a Python library for fast numerical computing created and released by Google. It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow.