About the Book
Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python—one of the world’s most popular and fastest-growing languages.
In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more.
- 500+ hands-on, real-world, live-code examples from snippets to case studies
- IPython + code in Jupyter® Notebooks
- Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code
- Rich Python coverage: Control statements, functions, strings, files, JSON serialisation, CSV, exceptions
- Procedural, functional-style and object-oriented programming
- Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames
- Static, dynamic and interactive visualisations
- Data experiences with real-world datasets and data sources
- Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression
- AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® Watson™, machine learning, deep learning, computer vision, Hadoop®, Spark™, NoSQL, IoT
- Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more
Table of Contents:
- Part 1-Python Fundamentals
- 1 Introduction to Computers and Python
- 2 Introduction to Python Programming
- 3 Control Statements; Program Development
- 4 Functions
- 5 Lists and Tuples
- Part 2-Python Data Structures, Files and Databases
- 6 Arrays
- 7 Sets and Dictionaries
- 8 Strings: A Deeper Look
- 9 File and Exceptions
- 10 SQL Databases
- Part 3-Python High-End Topics
- 11 Object-Based Programming: Classes and Objects
- 12 Object-Oriented Programming: Inheritance and Polymorphism
- 13 tkinter GUI
- 14 turtle Graphics and tkinter-Based Canvas Graphics
- 15 Concurrency and Parallelism
- 16 Game Programming with PyGame
- 17 Python Other Topics
- Part 4-Python-Based Data-Science Case Studies
- 18 Natural Language Processing (NLP)
- 19 Data Mining Twitter: Web Services and JSON
- 20 Supervised Machine Learning
- 21 Unsupervised Machine Learning
- 22 Deep Learning
- 23 Reinforcement Learning
- 24 NoSQL and NewSQL Databases
- 25 Big Data with Hadoop
- 26 Big Data with Spark; Internet of Things (IoT)
- 27 Special Feature: IBM Watson Analytics and Cognitive Computing
