This course with instructor Keith McCormick provides an introduction to some advanced techniques in causal inference and causal modeling. It builds upon a foundation in Keith's course, Machine Learning and AI Foundations: Prediction, Causality, and Statistical Inference. Keith focuses the course on three major topics: The power of experiments (and the reality that they aren't always available as an option); the Bayesian statistic philosophy and approach and when it's a good choice; and an introduction to causal modeling with techniques like structural equation modeling and Bayesian networks. Join Keith in this course to learn about these advanced techniques and what makes them both powerful and interesting.

One type of problem absolutely dominates machine learning and artificial intelligence: classification. Binary classification, the predominant method, sorts data into one of two categories: purchase or not, fraud or not, ill or not, etc. Machine learning and AI-based solutions need accurate, well-chosen algorithms in order to perform classification correctly. This course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy (or strategies) for their projects. Instructor Keith McCormick draws on techniques from both traditional statistics and modern machine learning, revealing their strengths and weaknesses. Keith explains how to define your classification strategy, making it clear that the right choice is often a combination of approaches.

Building world-class predictive analytics solutions requires recognizing that the challenges of scale and sample size fluctuate greatly at different stages of a project. How do you know how much data to use? What is too little, what is too much? How does your infrastructure need to scale with the volume and demands of the project? This course walks step by step through the strategic and tactical aspects of determining how much data is needed to build an effective predictive modeling solution based on machine learning and what volumes of data are so large that they will create challenges.