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Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference

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Keith McCormick

2:08:39

332 View
  • 01 - Prediction, causation, and statistical inference.mp4
    01:27
  • 01 - Lady tasting tea.mp4
    04:48
  • 02 - Why was it so difficult to establish causality.mp4
    06:09
  • 03 - Why causation matters in a business setting.mp4
    01:27
  • 04 - What is a causal model.mp4
    02:13
  • 01 - Skepticism about data Truman 1948 Election Poll.mp4
    03:07
  • 02 - Skepticism about results Is that really the best predictor.mp4
    03:47
  • 03 - Skepticism about causes Is X really causing Y.mp4
    02:59
  • 01 - What is a strong correlation.mp4
    06:53
  • 02 - Pearson on correlation and causation.mp4
    05:01
  • 03 - Correlation and regression.mp4
    05:06
  • 04 - Challenge What is causing what.mp4
    01:53
  • 05 - Solution What is causing what.mp4
    07:09
  • 01 - Using probability to measure uncertainty.mp4
    08:23
  • 02 - p-value review.mp4
    01:28
  • 03 - Hypothesis testing checklist.mp4
    03:59
  • 04 - Taleb on normality, mediocristan, and extremistan.mp4
    02:39
  • 05 - Challenge Evaluate significant finding.mp4
    01:50
  • 06 - Solution Evaluate significant finding.mp4
    05:34
  • 01 - What are induction and deduction.mp4
    04:21
  • 02 - Hume on induction.mp4
    03:35
  • 03 - Popper on induction and falsification.mp4
    04:32
  • 04 - Taleb on induction.mp4
    04:24
  • 05 - Counterfactuals Pearl on induction and causality.mp4
    02:24
  • 01 - Data mining vs. data dredging.mp4
    05:49
  • 02 - TrainTest What can go wrong.mp4
    04:57
  • 03 - AB testing during the evaluation phase.mp4
    02:45
  • 01 - The Two Cultures.mp4
    05:06
  • 02 - Explain vs. predict.mp4
    05:04
  • 03 - Comparing CRISP-DM and the scientific method.mp4
    04:35
  • 04 - Applying the two methods at work.mp4
    04:11
  • 01 - Review.mp4
    01:04
  • Description


    In the world of data science, machine learning and statistics are often lumped together, but they serve different purposes, and being versed in one doesn’t mean expertise in the other. In fact, applying a statistical approach to a machine learning problem, or vice versa, can lead to confusion more than elucidation. In this course, Keith McCormick covers how stats and machine learning are different, when to use each one, and how to use all the tools at your disposal to be clear and persuasive when you share your results. He covers topics like: Why correlation is insufficient evidence of causation; the difference between experimental and observational data; and the differences between traditional statistics and Bayesian statistics. Keith also looks at causality, a tricky topic when it comes to using statistics and machine learning to prove something causes something else. If you build machine learning models, run statistical analyses—or especially if you do both, this course is for you.

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    Keith McCormick
    Keith McCormick
    Instructor's Courses
    I'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 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 32
    • duration 2:08:39
    • Release Date 2023/01/18