Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Recommended route for learning Artificial Intelligence (AI),
Data Science (DS) and Internet of Things (IoT) in three stages:
Level 1: Literacy and Foundations in AI, DS and IoT
- Conceptual understanding of facts
- Able to interact with tools that enable or are driven by AI, DS and IoT
- Communicate about AI, DS and IoT at a basic level
Level 2: Contextual Knowledge of AI, DS and IoT
Level 3: Building AI, DS and IoT Solutions
Level 1: Literacy and Foundations
Knowledge for everyone – Technical and Non-Technical Roles
Business Stakeholder
Developer
Business Analyst
DevOps
Data Scientist
Data Engineer
What is
Data Science?
Which technology
capabilities
can I use?
What is Artificial
Intelligence?
What data
is required?
What are the
business goals?
How do I think
about AI in my
business?
Level 2: Contextual Knowledge
Leveraging pre-built frameworks – Technical Roles
Data Scientist
Developer
Data Engineer
DevOps
Data Science
Working with
complex data types
Sentiment Analysis
Tone and empathy
Human – AI
Interaction
Working with
complex documents
Visual Recognition
Working with
images and videos
Deep Learning
Frameworks
Keras, Pytorch, TensorFlow
Natural Language Processing
Scaling ML models
with Spark
Level 3: Building Solutions
Building models from scratch – Technical Roles
Data Scientist
Data Engineer
Mathematics
Probability, Statistics,
Linear Algebra
Data Preparation
Programming
Python, R, Scala
Data Visualization
Business Domain
Knowledge
Model Building
Supervised,
Unsupervised,
Deep, Reinforcement
Data Science
Methodologies
Model Validation
and Selection