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
Lecture Notes On Object Oriented Programming Through Python
This note describes the following topics: Introduction to Python and Object Oriented Concepts, Python Classes and Objects, Strings and Functions, Exception Handling, Graphical User Interface.
Author(s): Mr. M Purushotham Reddy and Mrs. A Lakshmi
119 Pages
A Gentle Introduction to Programming Using Python
This note will provide a gentle introduction to programming using Python for highly motivated students with little or no prior experience in programming computers. Topics covered includes: Variables and types, Functions, basic recursion, Control flow: Branching and repetition, Introduction to objects: Strings and lists, Structuring larger programs, Python modules, debugging programs, Introduction to data structures: Dictionaries, Functions as a type, anonymous functions and list comprehensions.
Author(s): Mihir Kedia and Aseem Kishore
NA Pages
The Python Tutorial by Madhavan Mukund
Python is an easy to learn, powerful programming language. This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature.
Author(s): Madhavan Mukund
NA Pages
Introduction to Python Programming Course Notes
This note covers the following topics: Basic Principles of Python, String Data, String Operations, Numeric Data, Types of Numeric Data, Conversion of Scalar Types, Lists, Tuples and Dictionaries, Input and Output, Programming, Functions, Using Modules, Writing Modules and Exceptions.
Author(s): Phil Spector
164 Pages
Introduction to Computer Science and Programming in Python
Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals.
Author(s): Dr. Ana Bell, Prof. Eric Grimson and Prof. John Guttag
NA Pages
This note covers the following topics: Native Datatypes, Comprehensions, Strings, Regular Expressions, Installing Python, Closures and Generators, Classes and Iterators, Advanced Iterators, Unit Testing, Refactoring, Files, XML, Serializing Python Objects, HTTP Web Services, Case Study: Porting chardet to Python 3, Packaging Python Libraries, Porting Code to Python 3 with 2to3 and Special Method Names.
Author(s): Mark Pilgrim
NA Pages
This reference manual describes the syntax and core semantics of the Python language. Topics covered includes: Lexical analysis, Data model, Execution model , Expressions, Simple statements, Compound statements, Top-level components and Full Grammar specification.
Author(s): Guido van Rossum and Fred L. Drake
128 Pages
This library reference manual documents Python's standard library, as well as many optional library modules . It also documents the standard types of the language and its built-in functions and exceptions, many of which are not or incompletely documented in the Reference Manual.
Author(s): Guido van Rossum, Python Software Foundation
NA PagesAdvertisement
