AI & Machine Learning for the Enterprise Overview (Hands-on)

Course Objectives

This course explores:

• 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

Course Prerequisites

This course is ideally suited for a wide variety of technical learners just getting started with AI or machine Learning, seeking a primer-level overview of these technologies, skills and related tools. 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.

• The hands-on labs in this leverage basic Python scripts as needed, but the labs can be completed in a ‘follow-along’ format, under the guidance of the instructor.  Prior experience with Python can be helpful but is not necessary.

Course Agenda

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

Exploring 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 the Crystal Ball