Artificial Intelligence (AI) Overview for the Enterprise

COURSE OUTLINE:

Description

Artificial Intelligence Overview for the Enterprise is a technical primer on the foundations of AI, introducing each sub-field of AI and how they can be practically exploited in the modern business sense.

Audience

This course is ideally suited for a wide variety of technical learners who need a fast paced, hands-on introduction to the core skills, concepts and technologies related to AI programming and machine learning.� Attendees might include:

  • Developers aspiring to be a 'Data Scientist' or Machine Learning engineers
  • Analytics Managers who are leading a team of analysts�
  • Business Analysts who want to understand data science techniques
  • Information Architects who want to gain expertise in Machine Learning algorithms�
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in Data Science and machine learning
  • Experienced professionals who would like to harness machine learning in their fields to get more insight about customers

Prerequisites

Students attending this class should have a grounding in Enterprise computing. While there�s no particular class to offer as a prerequisite, students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI.

Learning Objectives

This course introduces AI from a practical applied business perspective.�

Working in a hands-on learning environment, led by our expert AI course leader, students will explore and 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

Lesson: Artificial Intelligence

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

Lesson: Machine Learning

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

Lesson: Expert Systems

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

Lesson: Neural Networks

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

Lesson: Natural Language Processing

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

Lesson: Image, Video, and Audio Processing

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

Lesson: Sentiment Analysis

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

Lesson: Current Tools of the Trade

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

Lesson: What�s Next in AI

  • Current Developments
  • Gazing the Crystal Ball