What we're about

We’re excited to bring you the latest and practical technology on AI, Machine Learning, Deep Learning, Data Science and Big Data.

Our goal is to congregate with AI enthusiasts from all over Chicago to learn and practice AI tech, through tech talks, study jams, code labs etc.. we regularly invite tech leads from innovated companies, successful startups to share their practice experiences and practices in the world of AI, Cloud, Data, Blockchain.

If you’d like to speak at future meetups, co-promote your meetup or inquire about partnership opportunities, please feel free to reach out to us.

Thank you

Kelvin, Community Manager

===Tentative Agenda =====

5:30pm - 5:50pm, Snack & socia

5:50pm - 6:00pm, Intro/announcemen

6:00pm - 7:00pm, Tech Talk 1 and Q&A

7:00pm - 8:00pm, Tech Talk 2 and Q&A

8:00pm - 8:30pm, Lucky draw & Mingle

Upcoming events (4+)

4-Weeks AI course: Full Stack Deep Learning in AWS (Cohort 13)

This is paid online course (using zoom), follow instructions below to enroll.
AI live course: Full Stack Deep Learning in AWS (Cohort 13)
Start date: Sep 21st~Oct 14th,10AM PT/1PM ET, Every Tue/Thu.


In the AICamp online classroom (powered by Zoom), we will meet twice a week and you will interact with instructors and other classmates, listen the lectures, discuss problems; After class, you will work on projects, homework, and get support on private group on slack.

The course include:
* 4 weeks/ 8 sessions/ 12 hours
* 8 lectures / 8 hands-on projects
* Live Sessions, Real time interaction
* Capstone projects, work with peer students globally
* Slack supports to projects and homework

Many deep learning course cover theoretical techniques of algorithms and modeling. In this course, we will train you to become a Full Stack Deep Learning Engineer, capable of not just training models but also deploying and managing them in production for business value.
Building projects is the best way both to test the skills you have acquired and to demonstrate your newfound abilities to future employers.

You will learn deep learning primarily through building 8 production grade services, step by step. You will learn how to build production AI in AWS, how to integrate it with an application, and how to manage it through its lifecycle.
You will also build from scratch a custom project (where you will gather image data, train and tune a production grade neural network, build a prediction service, and connect that prediction service to create your own image processing AI application).

Students who take this course will be able to:
* Identify and frame business use cases that can be solved by deep learning
* Choose the right techniques, tools, frameworks to the business use cases
* Build production AI on AWS, and manage through their lifecycle.
* Hands on implementation of end to end AI for each use cases

AI Webinar: Scale a Data Literacy Program at Your Organization

*** Registration Instruction ****
* register on event website: https://www.aicamp.ai/event/eventdetails/W2021092311
* rsvp on meetup was turned off, you will NOT receive joining link if didn't register on event website.

Join data & analytics leaders from Starbucks, Cardinal Health, and Bol.com for a webinar panel discussion on scaling data literacy skills across your organization with a clear strategy, a pragmatic roadmap, and executive buy-in.
Our featured speakers will share practical guidance and examples from effective programs that help with data skill-building, improving decision-making, and fostering a data literate culture – so everyone at your organization can confidently read, write, analyze and communicate with data.

What you will learn in this webinar?
- Launching data literacy programs and building business cases.
- Best practices, pitfalls, practical first steps and models to scale adoption and continuously improve.
- Real-world examples and strategies with the people, process and technology investments needed to create a data literate culture.
- Unlocking enhanced levels of value and insight from data using a semantic layer.

Who should join?
Chief Data Officers, data scientists and engineers, business intelligence, and analytics professionals and leaders.

Featured speakers:
- Megan Brown, Director, Data Literacy, from Starbucks.
- Jenni Wheeler, Director of Data and Analytics, from Cardinal Health.
- Mariska Veenhof–Bulten, Business Intelligence Lead, from bol.com.
- Dave Mariani, Co-founder & Chief Technology Officer, from AtScale

Data Scientists Career Talk: From Academia to Industry

Online event

*** registration instruction ****
* please register on the event website to receive the joining link:
* RSVP on meetup only will NOT receive joining link.

* pre-event networking (10mins)
* community updates, jobs/interns/talents announcements. (5mins)
* tech talk (40mins)
* Q&A and open discussion (10mins)

In this talk, I will share my own experience transitioning from academia to the tech industry, from neuroscience to AI. I will discuss different types of data scientists and their career paths. Half of this talk will be in the Q&A format.
We will discuss:
- PhD career paths
- different types and skills for data scientists
- career path in tech

AI Webinar: Bridge Data Science And Business Intelligence

** Registration website:
(Free online tech event, you can join from anywhere. after register at the event website, you will receive the join link. also receive recordings if you miss the live sessions. thanks)

Join data and analytics leaders and strategists for a webinar panel discussion on data strategies for eliminating the silos between BI and data science teams using a semantic layer.
Our featured speakers will share practical guidance and examples from how they build a data infrastructure and architect a strategy to support both BI and data science programs. You will hear best practices for creating an analytics feedback loop between your data science and BI teams as well as how to create a common language for your data across the company.

What you will learn in this webinar?
- Gaining business alignment across BI and data science teams.
- Reducing data prep for your data science & BI teams.
- Creating governed and consistent access to features, numerics, categoricals, hierarchies, dimensions and measures.
- Simplify and accelerate feature engineering for more powerful ML models.

Who should join?
Data-driven leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals) looking to bridge the gap between data science and BI workloads and/or teams to make smarter decisions at scale.

Photos (120)