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.ï¿½ Attendees might include:

Developers aspiring to be a 'Data Scientist' or Machine Learning engineers
Analytics Managers who are leading a team of analystsï¿½
Business Analysts who want to understand data science techniques
Information Architects who want to gain expertise in Machine Learning algorithmsï¿½
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 thereï¿½s 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.ï¿½

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 isnï¿½t
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
Whatï¿½s 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: Whatï¿½s Next in AI

Current Developments
Gazing the Crystal Ball

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