Exploring AI / Artificial Intelligence & 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

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

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).