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
This note covers the following topics: Python syntax, Control flow, Functions, Odds and ends,Object-oriented programming, Exception handling,Type checking, Exception handling, main differences between Python 2.x and Python 3.x, Recursion, Functional programming, Command-line arguments, Generator.
Author(s): Mike Vanier
NA Pages
Introduction to Computer Programming Python
This note teaches the basics of programming in Python, which is an industrial-strength programming language used at companies like Google and Industrial Light and Magic. Topics covered includes: Python basics, Booleans, Strings, Modules, Loops, Lists, Dictionaries, Files, Classes, Sorting.
Author(s): Marek Ryszard Janicki
NA Pages
Python Introduction for Programmers
This note assumes that you know no Python whatsoever. This note covers Python 2.2 to 2.6, which are the most common versions currently in use– it does NOT cover the recently released Python 3.0 (or 3.1) since those versions of Python are so new.
Author(s): Bruce Beckles and Bob Dowling
151 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
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