Artificial Intelligence (AI) Overview for the Enterprise

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.

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

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.

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