Artificial Intelligence
and Machine Learning II.

Want to master the intricacies of Artificial Intelligence and Machine Learning?

Delve into 'Artificial Intelligence and Machine Learning II' and embark on a transformative journey through the advanced concepts of XAI, IML, and beyond.

In this comprehensive course, you'll uncover the nuances of Explainable AI (XAI) and Interpretability in Machine Learning (IML). From grasping the challenges of isolating a variable's contribution in Black Box Models to utilizing KNIME for XAI and IML, this course will enlighten you. Dive deep into XAI techniques, both global and local, and familiarize yourself with IML methods. Explore the foundations of experimental design, Bayesian statistics, and causal modeling using Structural Equation Modeling (SEM) and Bayesian Networks.

But that's not all. You'll discern the significance of p-values in hypothesis testing, demystify the differences between correlation and causation, and navigate through the challenges of multicollinearity. Engage with key concepts such as induction, deduction, falsification, and counterfactual, and understand their pivotal role in model evaluation. As you proceed, discover the subtleties of model performance testing in data mining and the fascinating world of Reinforcement Learning with its myriad algorithms.

By the end of this course, you'll be equipped with an in-depth understanding of the conflicting goals in statistics and data mining, as well as insights into methods like the Monte Carlo, Temporal Difference, and various modified forms of reinforcement.

If you're keen on being at the forefront of AI and Machine Learning, this course is your stepping stone to success. Embrace the future, today.