Welcome
  • About this Course: Machine Learning Fundamentals
  • What is Covered in this Course, Learning Support, & Discounts
  • Brief Instructor Bio
Part 1: Models, Machine Learning, Deep Learning, & AI Defined
  • Introduction to Part 1 & The definition of a Model
  • Example: A Basic Linear Regression Model
  • Initial High-Level Model Lifecycle & Model-Related Terminology
  • How our linear regression example fits the definition of a model
  • The Two Essential Model Types: Regression & Classification
  • Example: A Basic Decision Tree Classification Model
  • Wrapping Up Our Knowledge of Models
  • Optional: Model Parameters & Model Hyperparameters Defined
  • Models: A Key Component of a Learning Process
  • Updating Models
  • Machine Learning Defined
  • ML-Related Terminology
  • Optional: Old School Statistical Methods vs. New School Machine Learning Methods
  • Optional: Supervised vs. Unsupervised Learning
  • Deep Learning Defined
  • AutoML Defined
  • Artificial Intelligence (AI) Defined
  • Common Machine Learning Pitfalls
  • Optional (Humor): Model Training vs. Model Building
  • Conclusion of Part 1
Part 2: Identifying Use Cases
  • Introduction to Part 2
  • Common ML Misconceptions
  • Paths to Identifying Use Cases
  • Browsing / Gathering ML & AI Use Cases
  • Process Inspection
  • Process Inspection: Leverage Process Improvement Disciplines
  • Process Inspection: Unpack your KPIs
  • ML Themes
  • ML Theme 1: Replace Imperfect Rule-Based Systems
  • ML Theme 2: Breaking an Average
  • ML Theme 3: Allocate Limited Resources
  • ML Theme 4: Analyze Human Decisions
  • ML Theme 5: Analyze Activities at Scale
  • ML Theme 6: Predict Events
  • ML Theme 7: Predict People
  • AI & Chatbots
  • Conclusion of Part 2
Part 3: Qualifying Use Cases
  • Introduction to Part 3
  • Potential Disqualifier: Ethical Concerns
  • Potential Disqualifier: Fitness for Use
  • Feasibility Analysis
  • Feasibility Analysis: Establish a Business Hypothesis
  • Feasibility Analysis: Sketch Out the Business Process
  • Feasibility Analysis: Estimate High-Level ROI
  • Optional: Lock Down the Target & Population
  • Feasibility Analysis: Assessing Your Data - The Model Target
  • Feasibility Analysis: Assessing Your Data - Do we have enough data?
  • Feasibility Analysis: Assessing Your Data - Availability & Readiness
  • Feasibility Analysis: Assessing Your Data - Data Quality
  • Feasibility Analysis: Determine Model Requirements
  • Feasibility Analysis: Wrap-Up
  • Performance Measurement
  • Performance Measurement: Introduction to Analyzing Regressors
  • Performance Measurement: Mean Absolute Error (MAE)
  • Performance Measurement: Regression Simulation Example
  • Performance Measurement: Simulation of Repeated Processes
  • Performance Measurement: Historical Median Baseline
  • Performance Measurement: Summary for Measuring Regressors
  • Performance Measurement: Analyzing Classifiers
  • Performance Measurement: Limitations of Accuracy & Class Imbalance
  • Performance Measurement: Classifier Outcomes & Value
  • Performance Measurement: The Baseline for Classification Models
  • Performance Measurement: Using Probability of Class Membership
  • Performance Measurement: Summary for Measuring Classifiers
  • Optional: Assessing Classifier Calibration
  • Experimental Design
  • Conclusion of Part 3
Part 4: Building an ML Competency
  • Introduction to Part 4 & Organizational Context
  • Buying Software & Services
  • Buying Software & Services: Deployment Configurations
  • Buying Software & Services: Key Questions
  • Consulting Engagements
  • Infrastructure - MLOps
  • Infrastructure: Data Concerns
  • Infrastructure: Compute Concerns
  • Infrastructure: Cloud Computing
  • Infrastructure: AutoML
  • Conclusion of Part 4
Part 5: Strategic Take-aways
  • Introduction to Part 5 & Optimizing your Business
  • Optimizing your Business: Opportunities & Probabilities
  • Optimizing your Business: Human & Machine Learning
  • AutoML
  • Information Strategy
  • Conclusion