Machine Learning and AI Foundations: Causal Inference and Modeling
Focused View
Keith McCormick
2:51:14
298 View
001. Thinking about causality.mp4
01:43
002. What you should know.mp4
01:34
003. The investigator, the jury, and the judge.mp4
03:07
004. Fisher and experiments.mp4
04:59
005. John Snow and natural experiments.mp4
07:42
006. Double blind studies.mp4
02:15
007. Control variables (ANCOVA).mp4
11:11
008. Judea Pearl Problems with control variables.mp4
02:41
009. Moderation, mediation, and lurking variables.mp4
06:45
010. Simpson's paradox.mp4
08:41
011. Challenge Moderation, mediation, or a third variable.mp4
02:15
012. Solution Moderation, mediation, or a third variable.mp4
03:27
013. Turing, Enigma, and CAPTCHA.mp4
05:53
014. Enigma and uncertainty.mp4
04:07
015. Developing an intuition for Bayes with Wordle.mp4
08:36
016. Wordle and conditional probability.mp4
04:26
017. Wordle, bans, and bits.mp4
05:07
018. Wordle and Bayes' theorem.mp4
04:40
019. Challenge Conditional probability and Bayes' theorem.mp4
01:43
020. Solution Conditional probability and Bayes' theorem.mp4
02:45
021. Contrasting frequentist statistics and Bayesian statistic.mp4
04:54
022. Bayesian T-Test with JASP.mp4
12:20
023. Google Optimize.mp4
03:42
024. Bayes and rare events.mp4
04:26
025. Challenge JASP.mp4
02:01
026. Solution JASP.mp4
04:16
027. Sewell Wright.mp4
04:23
028. Introducing path analysis and SEM.mp4
02:55
029. SEM example Intention.mp4
04:16
030. Myths about SEM.mp4
04:16
031. Latent variables in SEM.mp4
02:49
032. Finding direction of causality with SEM (PSAT).mp4
03:35
033. Judea Pearl and the causal revolution.mp4
04:43
034. Downloading BayesiaLab and resources.mp4
02:37
035. Introducing BayesiaLab Hair and eye color.mp4
03:47
036. Introduction to causal modeling with Bayesian networks.mp4
06:27
037. Bayesian Networks Black Swan case study.mp4
03:33
038. Taking causality further.mp4
02:37
Description
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.
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Keith McCormick
Instructor's CoursesI'm an independent consultant, trainer, speaker, and author of seven books. My consulting specializes in helping analytics leaders build and manage their data science teams. My training, including 20 LinkedIn Learning courses and frequent conference workshops, has reached 1000s of individuals trying to learn statistics, machine learning, and data science.
I love that I am able to train and consult. Training allows me to interact with (and learn from) 100s of clients in dozens of industries every year. It prevents me from obtaining too narrow a focus, and it keeps me current. Consulting allows me to work with a smaller number of clients in detail and in-depth, working with them on real problems of immediate concern to them. It keeps me sharp.
If you've encountered me through my LinkedIn Learning courses, please consider following me here on LinkedIn. I'm not able to connect with everyone, so I connect only with clients and colleagues that I know directly. But please do follow me here because I'm quite active on LinkedIn and frequently post excerpts from the courses and other content. Follow #freefirstfridays to see when I post a link to watch a course for free.
My favorite kind of consulting work involves:
- working with analytics management to create effective data science teams
- listening carefully to my client explain their business in detail
- turning their description into a research question that can be answered with their data
- coaching my client on presenting possible solutions to decision-makers
- working behind the scenes to get the solution deployed
Specialties:
For the last several years, my emphasis has been working with analytics management to more efficiently run their teams and to nurture new hires as they expand their teams. I am skilled at explaining complex methods to new users or decision-makers and can do so at any level of technical detail. I specialize in predictive models and segmentation analysis, including classification trees, neural nets, general linear model, cluster analysis, and association rules.
Books and Courses
The best way to find out more about me is to check out my courses on LinkedIn Learning. They have received over 500,000 views, and each one has some free content. My books can be found on Amazon, and typically that allows you to view some free content as well. I'm very proud of all of this content (listed below in my profile), but I am still primarily an active consultant. If you need consulting help, private training, or a keynote speaker, contact me, and we can discuss.
Linkedin Learning
View courses Linkedin LearningLinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications.
It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
- language english
- Training sessions 38
- duration 2:51:14
- Release Date 2023/01/18
