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How to Think About Machine Learning Algorithms

Course Summary

If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.


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    Course Syllabus

    Course Overview
    - 1m 45s

    —Course Overview 1m 45s
    Introducing Machine Learning
    - 24m 43s

    —Recognizing Machine Learning Applications 5m 54s
    —Knowing When to Use Machine Learning 5m 14s
    —Understanding the Machine Learning Process 4m 44s
    —Identifying the Type of a Machine Learning Problem 8m 49s
    Classifying Data into Predefined Categories
    - 28m 44s
    Solving Classification Problems
    - 31m 37s
    Predicting Relationships between Variables with Regression
    - 16m 21s
    Solving Regression Problems
    - 20m 26s
    Recommending Relevant Products to a User
    - 27m 48s
    Clustering Large Data Sets into Meaningful Groups
    - 24m 49s
    Wrapping up and Next Steps
    - 12m 38s


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

This course is listed under Open Source , Development & Implementations and Data & Information Management Community

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