Exploring AI & Machine Learning Overview

Course Objectives

This course introduces AI from a practical applied business perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore:

• 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

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

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. Please see the Related Courses tab for recommended course pre-requisites, alternative suggested courses and follow on events.

Course Agenda

Exploring AI / Artificial Intelligence

• 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

Course Materials

Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable).