and Machine Learning II.
100 % Online | 1 month or less | 15 mins a day (Micro-learning)
15 Mins Per Day
About this course
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.
What you'll learn
- What are XAI and IML
- Black Box Model 101
- KNIME for XAI and IML XAI Techniques
- Conditional Probability and Bayes´ Theorem
- Causal Modeling with Structural Equation Modeling (SEM) and with Bayesian Networks
- Define causation, explain the difference between correlation and causation, and illustrate how to demonstrate causation
- Explain how to detect multicollinearity and describe a strategy for dealing with it
- Define induction, deduction, falsification, and counterfactual, and then illustrate their significance in model Evaluation
- Learning Algorithms, Monta Carlo Method
- and many more
Artificial Intelligence and Machine Learning II.
Duration: 7.5 hours (self-paced)
In every daily block you will find different types of study materials on the particular theme of the module. Lessons consist of:
- Audio-Visual materials
- Additional tips for relevant and interesting ebooks, videos or web pages
- Short tests to evaluate your progress
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Enroll this course by filling the form below and proceeding with a payment.
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OUR APPROACH TO ONLINE EDUCATION
Our courses consist of daily short lessons which follow the principles of microlearning. It is then easy to find time every day, maintain focus and learn what you need in practice.
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