Python® Data Science Full Throttle with Paul Deitel: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies
A One-Day, Presentation-Only, Case-Study-Intensive Seminar
In this live training for Python programmers, Paul introduces some of today's most compelling, leading-edge computing technologies with cool examples on 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 with a convolutional neural network, sentiment analysis through deep learning with a recurrent neural network, and big data with Hadoop®, Spark™ streaming, NoSQL databases and the Internet of Things. This is an aggressively paced, presentation-only, code-highlights and discussion seminar. There is no lab component. You’ll receive all the code and slides.
- You’ll leverage your Python skills to dive into some key Python data science, AI, big data and cloud technologies
- You’ll study many Python code examples, from individual snippets to highlights of fully implemented case studies
- You'll see how Python libraries for data science, AI, big data and cloud software technologies enable you to create powerful applications with minimal code quickly
What you'll learn-and how you can apply it
Paul will present programming case studies introducing the following data science, AI, big data, cloud and visualization technologies, libraries and tools:
- Natural Language Processing—TextBlob, Textatistic, spaCy and word_cloud
- Data Mining Twitter—Sentiment analysis, Tweepy, JSON, streaming tweets, word_cloud
- IBM Watson and Cognitive Computing—Building an inter-language speech-to-speech translator
- Supervised Machine Learning—Classification and linear regression with scikit-learn, Seaborn and Matplotlib
- Unsupervised Machine Learning—Clustering with scikit-learn
- Deep Learning for Computer Vision—Convolutional neural network in Keras running over TensorFlow
- Deep Learning for Sentiment Analysis—Recurrent neural network in Keras running over TensorFlow
- MongoDB NoSQL Document Database—Storing streaming tweets as JSON documents and visualizing with an interactive folium map
- Hadoop—MapReduce with Hadoop Streaming running on a Microsoft Azure cluster
- Spark—Spark and Spark Streaming running on a juypyter/pyspark-notebook Docker container
- Internet of Things (IoT) Streaming Data—Simulated streaming sensors with dweet.io, dweepy and PubNub; simulated streaming stock prices with PubNub; and visualizing streaming data with freeboard.io and Seaborn
This training course is for you because...
- You’re a Python developer and you see exciting AI, big data and data science technologies popping up everywhere and you want a one-day, code-based introduction to them
- You’re a Python developer looking to enhance your career opportunities with these current technologies
- You’re a manager contemplating Python projects using AI, big data and data science technologies and want a one-day, code-based introduction to them
- You’re an R developer whose organization is considering Python and you want a one-day, code-based introduction to Python’s AI, big data and data science capabilities
- Python programming experience
- Python® Full Throttle with Paul Deitel, one-day live training offered periodically here on Safari
- Lessons 1-10 of Paul’s new, in-depth video course Python® Fundamentals LiveLessons, which you can view here on Safari
- Chapters 1-10 of Paul’s new book Python® for Programmers, which you can read here on Safari
Note: Python code is easy to read, so even if you’re an experienced developer who does not know Python, you can still get a lot out of this course.
- No setup is required—this is a lecture-only presentation
- After the training, if you'd like to run the code, install the free Anaconda Python 3.x distribution (for macOS, Windows and Linux): https://www.anaconda.com/distribution/#download-section
- For examples that require additional setup, instructions are provided in Chapters 11-16 of Paul’s book Python® for Programmers and in Lessons 11-16 of Paul’s Python® Fundamentals LiveLessons videos, both available here on Safari.
- We recommend that you run the code examples using JupyterLab with the Jupyter Notebooks we provide. For a quick introduction to Jupyter Notebooks and JupyterLab, see either the Before You Begin lesson and Lesson 1 of Paul’s Python® Fundamentals LiveLessons videos or the Before You Begin section and Section 1.5 in Paul’s book Python® for Programmers.
Additional materials, downloads, supplemental content, or resources needed in advance:
- Paul will continue to answer your questions after the course at firstname.lastname@example.org. On the day of the course, Paul will provide links to download the slides and the code (in standard Python .py files and in Jupyter Notebooks .ipynb files).
- If you’re an instructor teaching college or professional Python courses, you may want to check out Paul’s new full-color textbook, Intro to Python® for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud. The textbook includes 240 pages of additional content with programming fundamentals for novices, 557 self-check exercises and 471 exercises and projects.
About your instructor
Paul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 38 years in computing. Paul is one of the world’s most experienced programming-languages trainers, having taught professional courses to software developers since 1992. He has delivered hundreds of programming courses to industry clients internationally, including Cisco, IBM, Siemens, Sun Microsystems (now Oracle), Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, Puma, iRobot and many more. He and his co-author, Dr. Harvey M. Deitel, are among the world’s best-selling programming-language textbook/professional book/video authors
The timeframes are only estimates and may vary according to how the class is progressing
- Natural Language Processing
- Data Mining Twitter
- IBM Watson and Cognitive Computing
- Machine Learning
- Deep Learning
- Big Data: Hadoop, Spark, NoSQL and IoT (Internet of Things)