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Machine Learning and AI Foundations: Clustering and Association

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Keith McCormick

3:22:11

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  • 01 - Welcome.mp4
    00:49
  • 02 - What you should know.mp4
    02:35
  • 03 - Using the exercise files.mp4
    01:37
  • 04 - What is unsupervised machine learning.mp4
    06:07
  • 01 - Looking at the data with a 2D scatter plot.mp4
    05:45
  • 02 - Understanding hierarchical cluster analysis.mp4
    05:55
  • 03 - Running hierarchical cluster analysis.mp4
    03:58
  • 04 - Interpreting a dendrogram.mp4
    04:14
  • 05 - Methods for measuring distance.mp4
    05:42
  • 06 - What is k-nearest neighbors.mp4
    05:05
  • 01 - How does k-means work.mp4
    02:03
  • 02 - Which variables should be used with k-means.mp4
    02:46
  • 03 - Interpreting a box plot.mp4
    06:49
  • 04 - Running a k-means cluster analysis.mp4
    03:28
  • 05 - Interpreting cluster analysis output.mp4
    05:42
  • 06 - What does silhouette mean.mp4
    02:20
  • 07 - Which cases should be used with k-means.mp4
    04:44
  • 08 - Finding optimum value for k k = 3.mp4
    05:07
  • 09 - Finding optimum value for k k = 4.mp4
    05:51
  • 10 - Finding optimum value for k k = 5.mp4
    05:03
  • 11 - What the best solution.mp4
    03:56
  • 01 - Summarizing cluster means in a table.mp4
    05:12
  • 02 - Traffic Light feature in Excel.mp4
    03:33
  • 03 - Line graphs.mp4
    07:00
  • 01 - Relating clusters to categories statistically.mp4
    06:23
  • 02 - Relating clusters to categories visually.mp4
    02:45
  • 03 - Running a multiple correspondence analysis.mp4
    06:10
  • 04 - Interpreting a perceptual map.mp4
    03:14
  • 05 - Using cluster analysis and decision trees together.mp4
    09:29
  • 06 - A BIRCHtwo-step example.mp4
    05:12
  • 07 - A self organizing map example.mp4
    07:43
  • 01 - The k = 1 trick.mp4
    07:07
  • 02 - Anomaly detection algorithms.mp4
    04:37
  • 03 - Using SOM for anomaly detection.mp4
    07:07
  • 01 - Intro to association rules and sequence analysis.mp4
    05:13
  • 02 - Running association rules.mp4
    05:57
  • 03 - Some association rules terminology.mp4
    03:33
  • 04 - Interpreting association rules.mp4
    07:18
  • 05 - Putting association rules to use.mp4
    05:04
  • 06 - Comparing clustering and association rules.mp4
    02:35
  • 07 - Sequence detection.mp4
    05:34
  • 01 - Next steps.mp4
    01:49
  • Description


    Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.

    Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.

    All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.

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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 42
    • duration 3:22:11
    • English subtitles has
    • Release Date 2023/04/11