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Artificial Intelligence (AI) Overview for the Enterprise | Hands-on

Learn how Artificial Intelligence is being applied in modern business.

Learn the foundations of Artificial Intelligence (AI), including its sub-fields, and how it can be applied in modern business. This course introduces AI from a practical business perspective.

 

GK# 7602
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Who Should Attend?

Business Analysts, Data Analysts, Developers, Administrators, Architects, Analytics Managers, and technical Executives who are new to AI.

What You'll Learn

Join an engaging hands-on learning environment, where you’ll learn:

  • What AI is and what it isn’t
  • The different types and sub-fields of AI
  • The differences between Machine Learning, Expert Systems, and Neural Networks
  • The latest in applied theory
  • How AI is used in processing language, images, audio, and the web
  • The current generation of tools used in the marketplace
  • What’s next in applied AI for businesses

This course has a 40% hands-on labs to 60% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

Course Outline

Artificial Intelligence

  • Definitions of AI
  • Types of AI
  • Mathematics in AI
  • Deep and Wide learning
  • AI and SciFi
  • AI in the Modern Age

Machine Learning

  • Supervised vs. Unsupervised
  • Classification
  • Regression
  • Clustering
  • Dimensionality Regression
  • Ensemble Methods

Expert Systems

  • Rules Systems
  • Feedback loops
  • RETE and beyond
  • Expert Systems in practice

Neural Networks

  • Neural Networks
  • Recurrent Neural Networks
  • Long-Short Term Memory Networks
  • Applying Neural Networks

Natural Language Processing

  • Language and Semantic Meaning
  • Bigrams, Trigrams, and n-Grams
  • Root stemming and branching
  • NLP in the world

Image, Video, and Audio Processing

  • Image processing and Identification
  • Facial Analysis
  • Audio Processing
  • Analyzing Streaming Video
  • Real-world AV processing

Sentiment Analysis

  • Sentiment: The beginnings of emotional understanding
  • Sentiment indicators
  • Sentiment Sampling
  • Algorithmic Trading on Sentiment
  • Predicting Elections

Current Tools of the Trade

  • Python, NumPy, Pandas, SciKit
  • Hadoop and Spark
  • NoSQL Databases
  • TensorFlow, Keras, and NLTK
  • Drools

What’s Next in AI

  • Current Developments
  • Gazing at the Crystal Ball
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Prerequisites

To gain the most from this course, you should have:

  • A grounding in enterprise computing
  • Familiarity with enterprise IT
  • A high-level understanding of systems architecture
  • Knowledge of your business drivers that could take advantage of AI
  • Basic knowledge of scripting

 

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