Applied AI & Machine Learning | Development



This course does not restrict or skew the presentation of machine learning methods through a single product. Rather, the Model Development course gives broad consideration of predictive analytics from a purely vendor-neutral perspective.

Live modeling demonstrations will precede follow-along exercises. Participants will directly experience the natural messiness of machine learning to discover what really works, as well as what doesn�t and why. The instructor will show how to evaluate various features and available products based upon strengths, limitations, function, value and general performance.

As active consultants on large organization AI implementations,�our instructors possesses a wealth of practical experience in applying predictive analytics across industries. This course, like no other, insists upon making predictive analytics purposeful, measurable and actionable in a larger and more complex organizational setting.


  • Data Scientists: who desire to extend their analytical toolbox with formal process and methodological practice at the organizational level
  • AI, Machine Learning and Predictive Analytics Practitioners: Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Social Media and Web Data Analysts, Fraud Detection Analysts, Audit Selection Managers, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers who desire to lead their teams and initiatives with greater functional confidence
  • Big Data Analysts: who are under increasing pressure to transform their deluge of data from a liability to an asset
  • Project Leaders: who want to gain a stronger command of predictive modeling methods and techniques to better manage and interact with practitioners
  • Business Analysts: who must develop and interpret the models, communicate the results and make actionable recommendations
  • IT Professionals: who wish to gain a better understanding of the data preparation, analytics and analytic sandbox development requirements to more fully support the growing demand for analytic IT support
  • Anyone overwhelmed with data and starved for actionable insights


While this course is designed to be taken independently, it is helpful to understand its place and function within the overall �The Predictive Analytics Operation� comprehensive course.�

Prior education or experience in analytics or statistics is helpful, but not required. Those seeking a deep drill-down into the mathematical or theoretical underpinnings of machine learning algorithms should refer to available academic or on-demand online offerings. The machine learning algorithms in this course are actively demonstrated and conveyed from a functional perspective.

Registrants will be required to view a three-hour online Core Concepts�orientation prior to attending this event. Instructions on how to download lab data and any analytic tools or platform will be provided in a preparatory email. The instructor can assist attendees with any preparation during breaks, and before or after class.

Learning Objectives

  • Apply a formal process for data preparation, model development and validation of results
  • Recognize and avoid common, costly pitfalls in data preparation, method selection and results interpretation
  • Ensure that your model is adequately generalized and has not memorized the training set
  • Take an incremental low-risk / high-impact approach to model development with vendor-neutral tool exposure
  • Better understand trade-offs between model accuracy and explainability when selecting modeling methods
  • Guide your IT staff to build an analytics sandbox for rapid model development and minimal IT dependency
  • Proceed confidently with the formal process, session files, and direct experience gained through follow-along labs, guided discussion and team engagement

Model Development Introduction

  • Current Trends in AI, Machine Learning and Predictive Analytics
    • Algorithms in the News: Deep Learning
    • The Modeling Software Landscape
    • The Rise of R and Python: The Impact on Modeling and Deployment<
    • Do I Need to Know About Statistics to Build Predictive Models?
  • Strategic and Tactical Considerations in Choosing a Modeling Algorithm
    • What is an Algorithm
    • Is a �Black Box� Algorithm an Option for Me?

The Tasks of the Model Phase

  • Generate Test Design
    • Train-Test Validation
    • Accept or Reject Modeling Parameters
    • Test / Test / Validate
  • Optimizing Data for Different Algorithms
  • Build Machine Learning Models
    • Classification
      • Issues Unique to Classification Problems
      • Why Classification Projects are So Common
      • An Overview of Classification Algorithms
        • Logistic Regression
        • Neural Networks
        • Na�ve Bayes Classification
        • Support Vector Machines
        • Decision Trees
        • Ensemble Methods
    • Value Estimation and Regression
    • Clustering
    • Association Rules
    • Other Modeling Techniques
      • Times Series
      • Text Mining
      • Factor Analysis
  • Model Assessment
    • Evaluate Model Results
      • Check Plausibility
      • Check Reliability
    • Model Accuracy and Stability
    • Lift and Gains Charts
  • Modeling Demonstration
    • Assess Model Viability
    • Select Final Models
  • Why Accuracy and Stability are Not Enough
  • What to Look for in Model Performance
  • Exercise Breakout Session
    • Select Final Models
    • Create & Document Modeling Plan
    • Determine Readiness for Deployment
  • What are Potential Deployment Challenges for Each Candidate Model?
  • Exercise Breakout Session and Guided Project Discussion

Wrap-up and Next Steps

  • Supplementary Materials and Resources
  • Conferences and Communities
  • Get Started on a Project!
  • Options for Implementation Oversight and Collaborative Development