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| Total size: |
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| Archived
files |
|
01 - Course Overview\01 - Course Overview.mp4
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02 - Getting Your Feet Ready to Run\02 - Overview.mp4
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|
2,016,463 |
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|
02 - Getting Your Feet Ready to Run\03 - What to Expect.mp4
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1,540,490 |
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|
02 - Getting Your Feet Ready to Run\04 - On Machine Learning.mp4
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02 - Getting Your Feet Ready to Run\05 - What Is Different About Machine Learning.mp4
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4,227,522 |
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02 - Getting Your Feet Ready to Run\06 - Learning Types.mp4
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11,843,319 |
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02 - Getting Your Feet Ready to Run\07 - Machine Learning Pipeline.mp4
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02 - Getting Your Feet Ready to Run\08 - Problem Definition.mp4
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4,518,199 |
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02 - Getting Your Feet Ready to Run\09 - Demo - Installing Python.mp4
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5,553,672 |
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02 - Getting Your Feet Ready to Run\10 - Summary.mp4
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|
03 - Feeding Your Machine Learning Pipeline\11 - Overview.mp4
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1,515,447 |
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03 - Feeding Your Machine Learning Pipeline\12 - Revisiting ML Pipeline.mp4
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03 - Feeding Your Machine Learning Pipeline\13 - Understanding Data Sourcing.mp4
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03 - Feeding Your Machine Learning Pipeline\14 - CSV Format.mp4
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03 - Feeding Your Machine Learning Pipeline\15 - Understanding SciPy.mp4
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03 - Feeding Your Machine Learning Pipeline\16 - Demo - Data Loading.mp4
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03 - Feeding Your Machine Learning Pipeline\17 - Summary.mp4
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04 - Understanding the Overall Data Trends\18 - Overview.mp4
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04 - Understanding the Overall Data Trends\19 - Revisiting ML Pipeline.mp4
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04 - Understanding the Overall Data Trends\20 - Introducing Data Analysis.mp4
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04 - Understanding the Overall Data Trends\21 - Univariant Numerical Analysis.mp4
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04 - Understanding the Overall Data Trends\22 - Bivariant Numerical Analysis.mp4
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04 - Understanding the Overall Data Trends\23 - Demo - Descriptive Stats.mp4
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04 - Understanding the Overall Data Trends\24 - Data Visualization.mp4
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04 - Understanding the Overall Data Trends\25 - Summary.mp4
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05 - Making Your Data Ready for the ML Model\26 - Overview.mp4
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|
1,974,705 |
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05 - Making Your Data Ready for the ML Model\27 - Revisting ML Pipeline.mp4
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05 - Making Your Data Ready for the ML Model\28 - Data Scaling - The Problem.mp4
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05 - Making Your Data Ready for the ML Model\29 - Data Scaling - The Solution.mp4
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05 - Making Your Data Ready for the ML Model\30 - The Need for Data Segregation.mp4
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05 - Making Your Data Ready for the ML Model\31 - Train Test Split.mp4
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05 - Making Your Data Ready for the ML Model\32 - KFlod Cross Validation.mp4
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05 - Making Your Data Ready for the ML Model\33 - Welcoming scikit-learn.mp4
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05 - Making Your Data Ready for the ML Model\34 - Demo.mp4
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05 - Making Your Data Ready for the ML Model\35 - Summary.mp4
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06 - Implementing Your Regression Solution\36 - Overview.mp4
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06 - Implementing Your Regression Solution\37 - Revisiting ML Pipeline.mp4
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06 - Implementing Your Regression Solution\38 - Scoping Your Focus.mp4
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06 - Implementing Your Regression Solution\39 - Introducing Derivatives.mp4
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06 - Implementing Your Regression Solution\40 - Linear Regression.mp4
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06 - Implementing Your Regression Solution\41 - Variance Bias Tradeoff.mp4
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06 - Implementing Your Regression Solution\42 - Other Regression Algorithms.mp4
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06 - Implementing Your Regression Solution\43 - Model Evaluation.mp4
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06 - Implementing Your Regression Solution\44 - Demo.mp4
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06 - Implementing Your Regression Solution\45 - Summary.mp4
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07 - What Is Next\46 - Overview.mp4
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07 - What Is Next\47 - Handling Features.mp4
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07 - What Is Next\48 - Model Improvement.mp4
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07 - What Is Next\49 - Automated ML.mp4
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07 - What Is Next\50 - Operationalization.mp4
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07 - What Is Next\51 - Team Data Science Process.mp4
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07 - What Is Next\52 - Summary.mp4
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building-trust-commitment-your-team.zip |
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01 - Course Overview |
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02 - Getting Your Feet Ready to Run |
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03 - Feeding Your Machine Learning Pipeline |
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04 - Understanding the Overall Data Trends |
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05 - Making Your Data Ready for the ML Model |
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06 - Implementing Your Regression Solution |
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07 - What Is Next |
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| Total size: |
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