Applied Machine Learning

Week 1

Modeling and Machine Learning Concepts

Focus on the basics, foundations, and concepts that are essential for progressing on the journey of expanding one’s expertise in ML techniques, algorithms, and applications, including supervised vs. unsupervised learning, feedback and optimization loops, accuracy vs. precision, benefits of high-variety data, bias and ethical modeling, types of analytics outcomes, and examples of successes and failures in the field.

Week 2

Common Business Applications of ML Algorithms

Focus on specific applications of ML algorithms to common business problems, including customer segmentation (personalization and recommender engines), outlier detection (anomaly, fraud, and surprise discovery), predictive analytics (forecasting, predictive maintenance), and association analysis (link discovery, knowledge graphs, marketing attribution).

Week 3

Uncommon (Atypical) Applications of Typical ML Algorithms

Focus on solving a business problem in an unexpected way with an ML algorithm or technique that is more commonly used for a very different problem or in a very different application domain. A common thread throughout this module is Forecasting 2.0 - going beyond traditional forecasting modeling techniques, into the realm of early warning detection within your business applications through precursor analytics.

Week 4

Analytics Mastery in Business and Beyond

Focus on the steps to analytics mastery, including matching the right algorithm and technique to the right business problem, data storytelling, decision science, the internet of things (IoT, which will produce massive quantities of real-time streaming data in the coming decade, generating a multi-trillion dollar market, requiring a data-literate analytics-equipped IoT-savvy workforce), and the soft skills that must accompany the hard skills for long-term career success and advancement.