What we’re about
* What your Meetup Group is about?
The focus of this Meetup group is to foster knowledge in the area of Big data and AI/ML/DL. Our goal is to share and educate people on varied topics within the Big data and Artificial Intelligence space.
* Who should join: Describe your ideal members?
Software Professionals - Anyone curious and interested in learning about Big data and AI/ML/DL.
It would range from people who are just curious George to folks who want to take Big data as profession/career.
Most of the sessions would be Webinar so location should not be a constraint for people to join.
* Why they should join: To learn, share, or have fun
Our passion is to help the world be more informed through these knowledge sharing and education sessions
* What members can expect: Describe typical activities that will foster in-person, face-to-face connections
This group is to foster learning of Big data and Artificial Intelligence technologies.
Upcoming events (1)
See all- Scraping and Sourcing Data with PythonLink visible for attendees$299.00
Enroll in this training and receive a one-month complimentary e-learning subscription with access to 40+ courses.
Event Title: Scraping and Sourcing Data with Python
📅 Date: 9 May 2025 & 16 May 2025
🕒 Time: 9-12 PM PST
📍 Location: Online (Zoom link will be provided to those RSVP'd at the time of the event)This course provides a thorough understanding of each of the key Python libraries used for data science -- NumPy, Pandas, Matplotlib and Scikit-learn, known as the Python data stack. We will perform data exploration, analysis, visualization and modeling.
In six, half-day sessions of hands-on training, you can quickly become a knowledgeable, productive, and efficient Data Science professional and earn a Stanford Technology Training Certificate of Proficiency in Data Science.
We will begin by discussing the data science process and how to effectively work through a data science problem. We'll talk about how to clean, transform, and prepare data for analysis. We will also cover descriptive and inferential statistics which will enable you to perform hypothesis testing so that you can better interpret the significance of your analysis. We will also focus on machine learning and predictive analytics. We'll discuss the various ways to measure model performance, how to select the best model for your project, and ways to refine that model.
