Part 1: Foundations

Start from zero. Build the vocabulary and mental models everything else depends on.

Module 1: What AI actually is, minus the hype

• AI vs. ML vs. deep learning
• Models, parameters, weights, training vs. inference
• Supervised, unsupervised, reinforcement learning
• Where AI is already running inside your company, whether approved or not.

Module 2: Neural networks and transformers

• Neurons and layers for engineers, not mathematicians
• Why transformers replaced everything before them
• Attention, explained so it clicks
• Training vs. running a model
• Why the cost profiles differ wildly.

Module 3: Inside a large language model

• Tokenization, and why it explains half the weird behavior you have seen
• Embeddings
• Context windows and the constraints they put on your design
• Temperature and sampling: the dials you will actually turn
• Why models hallucinate, and what that means for what you are allowed to build

By the end of Part 1 you can read any AI claim, vendor pitch, or job posting and know exactly what is being said, and predict where a model will fail before you write code.

Part 2: The Big Data Prerequisite

1. Evaluating your big data practice
2. State of tools – understanding intelligent big data stacks
   1. Visualization and Analytics
   2. Computing
   3. Storage
   4. Distribution and Data Warehousing
3. Strategically restructuring enterprise data architecture for AI
4. Unifying data engineering practices
5. Datasets as learning data
6. Defeating Bias in your Datasets
7. Optimizing Information Analysis
8. Utilizing the IoT to amass a large amount of data

Part 3: Implementing Machine Learning

1. Examine pillars of a practicing AI team
   1. Business case
   2. Domain expertise
   3. Data science
   4. Algorithms
   5. Application integration
2. Bettering Machine Learning Model Management
3. State of tools – understanding intelligent machine learning stacks
4. Machine Learning Methods and Algorithms
   1. Decision Trees
   2. Support Vector Machines
   3. Regression
   4. Naïve Bayes Classification
   5. Hidden Markov Models
   6. Random Forest
   7. Recurrent Neural Networks
   8. Convolutional Neural Networks
5. Developing Validation Sets
6. Developing Training Sets
7. Accelerating Training
8. Encoding Domain Expertise in Machine Learning
9. Automating Data Science
10. Deep Learning

Example: TensorFlow – We will take a look at Google’s TensorFlow as a tool for integrating machine learning features. We’ll come away from the exercise with an understanding of the programming skills needed to leverage TensorFlow and the impacts of normal application workflow.

Part 4: Creating Concrete Value

1. Opportunities for automation
2. Understanding automation vs. job displacement vs. job creation
3. Finding hidden opportunities through improved forecasting
4. Production and operations
5. Adding AI to the Supply Chain
6. Marketing and Sales Applications
   1. Predict Customer Behavior
   2. Target Customers Efficiently
   3. Manage Leads
   4. AI-powered content creation
7. Enhancing UX and UI
8. Next-Generation Workforce Management
9. Explaining Results

Use case breakout: Scoring the criteria for three potential applications. In groups, we’ll evaluate application use cases for machine learning: Medical imaging, electronic medical records, and genomics. We’ll grade each use case based on a scorecard for the following:

• Quantity of data
• Quality of data
• ML techniques

This artificial intelligence course is designed for professionals involved in selecting, designing, approving, or implementing AI-enabled systems including:

• Software engineers and technical leads
• Software and solution architects
• Engineering managers and directors
• Platform and DevOps professionals
• Technical consultants and solution engineers
• Product and technical product leaders
• Technology executives who need enough technical depth to make sound implementation decisions

Programming experience is helpful but not required.

• Identify business problems where AI adds value and recognize problems better solved with conventional software.
• Compare frontier, smaller, local, and private model approaches.
• Choose among prompting, RAG, tools, agents, and other common AI application patterns.
• Understand how AI systems connect to enterprise knowledge and operational systems.
• Evaluate output using deterministic checks, AI-based evaluation, escalation, and human review.
• Understand the practical role of MCP in connecting AI systems to tools and data.
• Evaluate and control cost alongside latency, privacy, security, quality, and reliability.
• Recognize common production risks including hallucination, prompt injection, data leakage, and uncontrolled automation.
• Create a practical implementation plan for an enterprise AI use case.