Understanding AI / Artificial Intelligence & Machine Learning / Overview (TTML5500)


Course Overview

The Exploring AI & Machine Learning for the Enterprise Overview is an introductory-level, lecture, demonstration and group discussion style course that explores the foundations of AI and how AI can be practically exploited in the modern business sense. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.

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

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

Outline: Understanding AI / Artificial Intelligence & Machine Learning / Overview (TTML5500)

Session: The AI Landscape

Lesson: Overview: Data Science to Deep Learning

  • Data Science versus Machine Learning
  • Types of AI
  • Machine Learning
  • Deep Learning

Lesson: Diving into Machine Learning

  • Importance of Data
  • Supervised Learning Explained
  • Classification
  • Regression
  • Unsupervised Training
  • Clustering
  • Dimensionality Reduction

Lesson: Real-World Use Cases for ML and AI

  • Retail
  • Financial
  • Healthcare
  • Manufacturing
  • Self-Driving Cars

Lesson: Real-World Expectations for ML and DL

  • Challenging Machine Learning
  • Simplicity and Data
  • Structured vs. Unstructured Data
  • Challenging Deep Learning
  • Today’s Limit on Image Recognition
  • Today’s Limit in Natural Language Processing
  • Data, Data, and More Data

Session: Machine Learning Projects

Lesson: Step 1: Plan

  • ML Project Workflow
  • Selecting a Business Need
  • Data Science vs Machine Learning
  • ML Planning Specifics
  • ML Considerations

Lesson: Step 2: Data Management

  • Acquiring and Analyzing Data
  • Pre-Processing Data
  • Data Transformations
  • Unstructured to Structured
  • Dimensionality Reduction

Lesson: Step 3: Feature Selection and Engineering

  • Steps May Iterate and Change Order
  • Defining Feature Selection
  • Examples to Consider
  • Raw Data to Algorithm Inputs

Lesson: Step 3: Feature Selection and Engineering

  • Data Drivers for Selection
  • Structured and Unstructured
  • Supervised and Unsupervised
  • Goal Drivers for Selection
  • Classification and Regression
  • Clustering and Dimensionality Reduction
  • ML Algorithms

Lesson: Step 4: Model Training and Validation

  • Importance of Getting to Supervised Training
  • Training and Testing Datasets
  • Training the Model
  • Testing the Model

Lesson: Step 5: Implementation to Production and Monitoring

  • Consuming the Model Results
  • Considerations for Deploying the Model
  • Monitoring to Make Further Improvements

Session: Setting the Stage

Lesson: Current Tools of the Trade

  • Python and Its Libraries
  • ML Libraries Including SciKit-learn
  • DL Libraries Including TensorFlow and Keras
  • Hardware From GPUs to TPUs
  • Open Source Datasets
  • Resources to Experiment With

Lesson: Deep Learning: A Primer

  • Neural Networks (NN) for Handling Tougher Problems
  • Basic NN for Demand Prediction
  • DL and Unstructured Data
  • Advances in Image Processing
  • Convoluted NN
  • Advances in NLP
  • Recurrent NN

Prices & Delivery methods

Online Training

1 day

  • on request
Classroom Training

1 day

  • on request


Currently there are no training dates scheduled for this course.