Python for Everybody by Dr. Charles R. Severance
This PDF Python for Everybody by Dr. Charles R. Severance covers the following topics related to Python : Why should you learn to write programs?, Variables, expressions, and statements, Conditional execution, Functions, Iteration, Strings, Files, Lists, Dictionaries, Tuples, Regular expressions, Networked programs, Using Web Services, Object-oriented programming, Using Databases and SQL, Visualizing data.
Author(s): Dr. Charles R. Severance
249 Pages 
Introduction to Python for Computational Science and Engineering
This note covers the following topics: A powerful calculator, Data types and data structures, introspection, Input and output, Control flow, Function and modules, Functional tools, Common tasks, Python shells, Symbolic computation, Numerical Computation, Numerical python arrays, Visualising data, Numerical method using python, Pandas data science with python, Python packages and environments.
Author(s): Hans Fangohr
267 Pages
Lecture note on Python Programming
This note covers introduction to python and history, Python Structure with basic collation, Object orientated concept and exception handling with debugging and file handling.
Author(s): Vitthalbhai Patel and Rajaratna P T Patel Science College
112 Pages
This lecture note covering basic to advanced concepts of python ,program execution, arithmetic, variables and functions. It also explores turtle module case studies, conditionals, recursion, functions, iteration, strings, and lists with a focus on algorithms, dictionaries, tuples, file handling, and basic object-oriented programming, classes, functions, inheritance, and advanced Python features.
Author(s): S Venkata Lakshmi
187 Pages
Lecture notes on python functions
This book explains the following topics: Introduction and Review, variables, Expressions, Operators, For Loops, Range For Loops, Python Functions : Karel functions, function Analogy, Function as python Objects and Variable Scope.
Author(s): NA
116 Pages
Python Machine Learning Projects
The contents include : Foreword, Setting Up a Python Programming Environment, An Introduction to Machine Learning, How To Build a Machine Learning Classifier in Python with Scikit-learn, How To Build a Neural Network to Recognize Handwritten Digits with TensorFlow, Bias-Variance for Deep Reinforcement Learning: How To Build a Bot for Atari with OpenAI Gym.
Author(s): Lisa Tagliaferri, Michelle Morales, Ellie Birbeck, and Alvin Wan
135 Pages
Python Programming Course Material
The contents include: Introductions, Operators in Python, Input and Output Statements, Control flow Statements, Strings, Files, Lists, Sets, Tuples, Dictionaries, Functions, Modules.
Author(s): Mr. P. Prathap Naidu
376 Pages
