The Deitel® Python® Guide for the Professional Programmer with introductory case studies in artificial intelligence. Written for programmers with a background in another high-level language, this book uses hands-on instructions to teach the latest and most advanced information technology and programming techniques with Python – one of the most popular and rapidly growing languages.

Within the 500+ real-world examples, from individual snippets to 40 large scenarios and full application case studies, you will use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the most modern idioms of Python code. After covering Chapters 1-5 on Python and some key parts of Chapters 6-7, you will be able to tackle significant portions of the experiential introductory case studies on AI in Chapters 11-16, which contain a wealth of smart, powerful, modern examples.

These include natural language processing, Twitter® data mining for sentiment analysis, cognitive computing with IBM® WatsonTM, 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®, SparkTM, and NoSQL databases, the Internet of Things, and more. You will work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub, and others.

Features

• 500+ experiential, realistic, live code examples, from snippets to case studies
• IPython + code in Jupyter® Notebooks
• Focused on Libraries: Uses the Python Standard Library and data science libraries to accomplish significant tasks with minimal code
• Rich Python coverage: Control statements, functions, strings, files, JSON and CSV serialization, exceptions
• Procedural, functional programming, and object-oriented programming
• Collections: Lists, tuples, dictionaries, sets, NumPy array (arrays), pandas Series & Dataframe
• Static, dynamic, and interactive visualizations
• Data experiences with datasets and real-world data sources
• Introduction to Data Science modules: AI, basic statistics, simulation, animations, random variables, data preprocessing, regression
• AI, big data, and cloud data science case studies: NLP, Twitter® data mining, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT
• Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras, and others.