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

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

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.

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

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