Machine Learning for Entrepreneurs Building Smarter Products & Services #254040

Upon completion of this course, participants will be able to:

• Articulate the Business Value of ML: Clearly explain how ML can solve specific customer problems, create new revenue streams, and build a competitive moat.

• Identify and Prioritize ML Use Cases: Systematically evaluate business processes and product features to find the highest-value opportunities for ML integration.

• Navigate the Technical Landscape: Understand the different types of ML (supervised, unsupervised, reinforcement learning) and their common business applications without needing to code.

• Make Strategic Technical Decisions: Evaluate the build-vs.-buy-vs.-partner dilemma for ML capabilities and understand how to work effectively with data scientists and engineers.

• Develop an ML Product Roadmap: Create a phased plan for integrating ML into a product, from initial data collection and prototyping to full-scale deployment.

• Understand Data Needs and Ethics: Identify the data required to power ML features and recognize the ethical responsibilities and potential biases involved.

This course is designed for the builders and visionaries of the business world:

• Startup Founders & Co-Founders

• Product Managers & Product Owners in early-stage companies

• Innovation Managers & Corporate Intrapreneurs

• Business Development Professionals exploring tech-driven ventures

• Tech-Savvy Consultants & Advisors to startups

• Venture Capital Associates & Angel Investors who want to better evaluate AI-driven startups

• Anyone with a business idea who wants to leverage technology to make it smarter and more scalable.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Demystifying ML: The Entrepreneur’s Strategic Advantage

• AM: Why ML is a Business Game-Changer

  • Beyond the hype: Real-world examples of ML driving startup growth and valuation.

  • How ML creates defensible moats: network effects, data flywheels, and personalization.

  • The language of ML: Key terms (models, training, inference, algorithms) explained in plain English.

• PM: The ML-Powered Business Model

  • Mapping ML to business model canvases: new value props, revenue streams, and cost structures.

  • Workshop: Ideation session – brainstorming ML opportunities for a sample business idea.

Day 2: Finding the Right Problem: Use Case Identification

• AM: The ML Opportunity Landscape

  • A tour of common ML applications: Recommendation systems, predictive analytics, churn prediction, dynamic pricing, NLP, and computer vision.

  • How to spot an ML opportunity: Is a task repetitive, data-rich, and requiring pattern recognition?

• PM: Prioritizing Your ML Initiative

  • Framework for evaluation: Impact vs. Feasibility (Data, Complexity, Cost).

  • Hands-on Exercise: Participants bring their own business concept and use the framework to identify and prioritize their top ML use case.

Day 3: Building Your ML Toolbox: A Non-Technical Guide

• AM: How ML Works (Without the Math)

  • Supervised Learning: Learning from labeled data (e.g., spam detection, sales forecasting).

  • Unsupervised Learning: Finding hidden patterns (e.g., customer segmentation, anomaly detection).

  • The crucial role of data: quality, quantity, and labelling.

• PM: The Build vs. Buy Decision

  • Overview of the ecosystem: Building in-house, using cloud APIs (e.g., AWS, Google Cloud, Azure), and partnering with specialty firms.

  • Calculating the Total Cost of Ownership (TCO) for each path.

  • Case Study: How a successful startup chose its ML path.

Day 4: From Concept to Prototype: The ML Product Lifecycle

• AM: The MVP Approach to ML

  • The ML development process: Data collection -> Model training -> Evaluation -> Deployment.

  • Creating a “Wizard of Oz” MVP to test an ML concept before full build-out.

  • Key metrics: How to measure the performance of your ML feature (e.g., accuracy, precision, recall).

• PM: Data Strategy and Ethics

  • How to acquire, label, and manage data ethically and legally.

  • Identifying and mitigating bias in ML systems.

  • Building trust with users: transparency and explainability.

Day 5: Go-to-Market and Capstone

• AM: Selling Your Intelligent Product

  • How to market an ML-powered product and communicate its value to customers.

  • Pitching to investors: How to articulate your tech advantage and data strategy.

  • Workshop: Crafting a 60-second pitch for an ML feature.

• PM: Capstone Project: Your ML Action Plan

  • Final Exercise: Participants finalize a one-page plan for their prioritized ML use case, including:

    • Problem Statement & Value Proposition

    • Data Strategy

    • Technical Approach (Build/Buy/Partner)

    • Key Metrics for Success

    • Next Steps & 3-Month Roadmap

  • Presentations and feedback from peers and instructors.

  • Course Wrap-Up: Resources and next steps for your ML journey.