Python® Data Science & AI Full Throttle with Paul Deitel: Introductory AI, Big Data, Cloud & GenAI Case Studies

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, DeepL, spaCy and word_cloud
• Data Mining Social Media — Sentiment analysis, Mastodon.py, JSON, getting user account info, searching for hashtags, streaming live posts, interactive folium maps
• Supervised Machine Learning — Computer vision with classification using scikit-learn and Seaborn and Matplotlib visualizations
• Unsupervised Machine Learning — Using scikit-learn dimensionality reduction to help visualize multidimensional data; clustering with scikit-learn--Deep Learning for Computer Vision — Convolutional neural network using Keras in TensorFlow
• MongoDB NoSQL Document Database — Storing data as JSON documents and visualizing with an interactive folium map
• Spark — Spark and Spark Streaming
• Internet of Things (IoT) Streaming Data — Simulated streaming sensors with PubNub; simulated streaming stock prices with PubNub; and visualizing streaming data
• Building OpenAI API-Based Python GenAI Applications — Summarize documents, sentiment analysis, vision, text translation, generate and manipulate Python code, named entity recognition, speech-to-text, text-to-speech, create original images, transfer art styles to images via text prompts, transfer styles between images, generate video closed captions, filter inappropriate content

This live event is for you because...

• You’re a Python developer and you see exciting AI, big data, data science and genAI 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, data science and genAI 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, data science and genAI capabilities

Prerequisites

• Python 3 programming experience

Course Set-up:

• No setup is required—this is a lecture-only presentation. Optional resources are below.
• Get the code
• You can run the examples in a zero-install environment at: https://mybinder.org/v2/gh/pdeitel/PythonDataScienceFullThrottle2e/main?urlpath=lab
• Docker users can build a local container from the Dockerfile in the GitHub repository. These instructions assume you have Docker Desktop installed with support for the docker compose command and that you've downloaded or cloned this repository to your system.

Comment out the Dockerfile line:

• COPY . /home/jovyan/ by inserting a # before the line as in #COPY . /home/jovyan/ (or simply delete that line.)
• From a Terminal window (Mac) or a Command Prompt or Powershell window (Windows) change to the root folder of the repository on your system, then execute: docker compose up
• Once this finishes building the container, which can take several minutes depending on your connection speed, you'll see a line of text similar to the following:
• http://127.0.0.1:8888/lab?token=fb59401a105a0c5a45c52eff8e1a8469f508cad1f3a8be06
• Copy your system's version of this line and paste it into your preferred web browser to launch JupyterLab.

Additional materials, downloads, supplemental content, or resources needed in advance:

• Paul will continue to answer your questions after the course at paul@deitel.com. 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 full-color textbook, Intro to Python® for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud. https://learning.oreilly.com/library/view/intro-to-python/9780135404799/. The textbook includes 240 pages of additional content with programming fundamentals for novices, 557 self-check exercises and 471 exercises and projects.

Recommended preparation

• Attend: Python® Full Throttle with Paul Deitel by Paul Deitel
• Watch: Lessons Lessons 1-10 of Python® Fundamentals LiveLessons by Paul Deitel
• Read: Chapters 1-10 of Intro to Python® for Computer Science and Data Science by Paul Deitel
• Read: Python® for Programmers by Paul Deitel