Course

Discover the course platform that I2AI created for you and your company to become leaders in digital transformation processes! With content options for all audiences who want to learn about Artificial Intelligence, from strategies for professionals who lead the projects to technical modules for technology professionals who will implement these technologies.

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Non-profit

Our educational vertical supports the sustainability of all our projects in the non-profit association, all the resources raised are transformed into more content and knowledge for our society.

In person & On-Line Trails

Certificado AI Technologies Foundations
November 2021
7 p.m. - 7 p.m.

What will you learn:

  • 11/04/2021 - RPA (Robot Process Automation) - Alexandre Seidl
  • 11/11/2021 - Machine Learning - Diego Nogare
  • 11/18/2021 - Computer Vision - Alessandro Faria (Cabelo)
  • 11/25/2021 - NLP (Natural Language Processing) - Alexandro Romeira.
RPA (Robotic Process Automation)
Nov. 4, 2021
7 p.m. - 11 p.m.

What will you learn:

  • How to assess the potential adherence of candidate processes to automation with RPA
  • How to identify the benefits to be obtained and the complexity of the processes for automation
  • How to create a process automation roadmap with RPA
  • What are the architectural components of an RPA platform
  • Good automation development practices with RPA
Machine Learning
Nov. 11, 2021
7 p.m. - 11 p.m.

What will you learn:

  • Supervised Learning
  • Classification
  • Regression
  • Unsupervised learning
  • Clustering
Computer Vision
Nov. 18, 2021
7 p.m. - 11 p.m.

What will you learn:

  • Understanding image acquisition
  • Understanding the impact of the hardware environment
  • Processing, filters, segmentation and morphology concepts
  • Pattern recognition and Convolutional Neural Network
  • Understanding the development of applications for object recognition.
NLP (Natural Language Processing)
Nov. 25, 2021
7 p.m. - 11 p.m.

What will you learn:

  • Introduction to natural language processing
  • Text normalization (tokenization, stemming and stopwords)
  • Exploring text data
  • Uses and applications in Natural Language Processing
  • Development and discussion of a basic Machine Learning model for text classification

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