Artificial Intelligence (AI) Overview

PT16773
Summary
This class is designed for attendees who do not have any prior exposure to AI, machine learning, predictive analytics, or related fields. Key concepts and processes are explained, along with critical decision points for deploying AI solutions in a business context. Code demos are minimal and can be done with Python (local) or with Hadoop/Spark and Python/Scala (distributed).
Prerequisites
There are no prerequisites for this course.
Duration
1 Day/Lecture & Lab
Audience
This class is designed for attendees who do not have any prior exposure to AI, machine learning, predictive analytics, or related fields.
Topics
Introduction
  • Data
  • Regression vs. Classification
  • Data Preparation / Feature Engineering
  • Model Evaluation and Tuning
  • Supervised vs. Unsupervised Training
  • Sequential vs. Distributed Training
  • Deep Learning
  • Reinforcement Learning
  • Approaches to AI for Natural Language Processing
  • Research vs. Deployment

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