Machine Learning in Business

Modules Overview

8 Modules

Module 1. Introduction

Start your journey by exploring the structure of the course and understanding how machine learning applies to real-world business challenges. You’ll review business metrics, model evaluation, data collection and get an overview of the final project. This module sets the stage for aligning machine learning knowledge with business value.

Module 2. Entry Assessment

Evaluate your current skills in machine learning for business through a structured entry test. You’ll identify your strengths and areas for growth, and receive tailored recommendations for how to get the most out of the course.

Module 3. Business Metrics

1.5h

Explore how to define and calculate key business indicators such as profit, margin, conversions, and funnels. You’ll learn to distinguish between online and offline metrics and analyze how they connect to machine learning outcomes. Throughout this module, you’ll work through case studies to interpret and apply these business indicators in realistic scenarios.

Module 4. Implementing New Functionality

Discover how to assess whether new product features or processes will improve performance. You’ll learn the principles of A/B testing, how to interpret its results, and how to estimate confidence intervals. You’ll also master the Bootstrapping technique to evaluate metric variability. Case studies in this module will help you practice selecting and applying these evaluation methods to business decisions.

Module 5. Data Collection

Understand the foundations of collecting high-quality data for Machine Learning. You’ll learn how to control the quality of data labeling through majority voting, avoid target leakage, and use cross-validation to enhance model reliability. Real-world scenarios will guide your exploration of different data collection techniques and how they impact model performance.

Module 6. Course Project

Apply your knowledge in a comprehensive real-world challenge. You’ll work for a fictional mining company, OilyGiant, and your task is to identify the most profitable locations to develop 200 new oil wells. You’ll analyze datasets from three regions, build a predictive model to estimate reserves, select the most promising wells, and recommend the region with the highest total profit.

Module 7. Final Assessment

Complete a final test to assess what you’ve learned and compare your performance with the entry test. The test is divided into skill-based sections, and you’ll receive immediate results and insights into your progress across all key topics.

Module 8. Conclusion

Wrap up the course by reviewing key concepts, reflecting on your learning experience, and providing feedback to help improve future course iterations.

Machine Learning in Business

Key Program Takeaways

Modules Overview

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