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

SS Course: 2001633

Course Overview


AI & Machine Learning for the Enterprise Overview (Hands-on) is a high-level 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 introduces AI from a practical applied business perspective. This course is ideally suited for a wide variety of technical learners just getting started with AI or machine Learning, seeking a ‘first-look’ exposure level course that includes some light hands-on.


Scheduled Classes


What You'll Learn


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


Viewing outline for:

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



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.

    Who Should Attend


    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

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