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| Archived
files |
|
01 - Course Overview\01 - Course Overview.mp4
[a31c84f3464009a7]
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02 - Getting Started with Azure Machine Learning\02 - Introduction.mp4
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4,132,931 |
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02 - Getting Started with Azure Machine Learning\03 - What Is Machine Learning-.mp4
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4,908,900 |
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02 - Getting Started with Azure Machine Learning\04 - Introduction to Azure Machine Learning.mp4
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3,534,023 |
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02 - Getting Started with Azure Machine Learning\05 - Azure Machine Learning Experiment Workflow.mp4
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3,311,264 |
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02 - Getting Started with Azure Machine Learning\06 - Prerequisites.mp4
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02 - Getting Started with Azure Machine Learning\07 - Demo- Creating an Azure Machine Learning Studio Workspace.mp4
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11,899,644 |
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02 - Getting Started with Azure Machine Learning\08 - Demo- Creating an Azure Machine Learning Service Workspace.mp4
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|
7,573,292 |
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|
02 - Getting Started with Azure Machine Learning\09 - Demo- Exploring the Dataset.mp4
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9,698,869 |
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02 - Getting Started with Azure Machine Learning\10 - Summary.mp4
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|
1,230,108 |
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03 - Differentiating Data, Features, Targets, and Models\11 - Introduction.mp4
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1,165,540 |
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03 - Differentiating Data, Features, Targets, and Models\12 - Moving from Raw Data to Features.mp4
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2,359,729 |
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03 - Differentiating Data, Features, Targets, and Models\13 - 6 Characteristics of a Good Feature.mp4
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03 - Differentiating Data, Features, Targets, and Models\14 - Define Target for ML Problems.mp4
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03 - Differentiating Data, Features, Targets, and Models\15 - Demo- Exploring Datasets for Different Problems.mp4
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03 - Differentiating Data, Features, Targets, and Models\16 - How Algorithms Learn Models.mp4
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03 - Differentiating Data, Features, Targets, and Models\17 - Demo- Modifying the Metadata of Datasets.mp4
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03 - Differentiating Data, Features, Targets, and Models\18 - Summary.mp4
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04 - Preparing Input Data for Machine Learning Models\19 - Introduction.mp4
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04 - Preparing Input Data for Machine Learning Models\20 - Data Preprocessing Methods.mp4
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04 - Preparing Input Data for Machine Learning Models\21 - Entropy-based Discretization.mp4
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04 - Preparing Input Data for Machine Learning Models\22 - Demo- Entropy-based Discretization.mp4
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04 - Preparing Input Data for Machine Learning Models\23 - Summary.mp4
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04 - Preparing Input Data for Machine Learning Models\24 - Demo- Exploratory Data Analysis.mp4
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04 - Preparing Input Data for Machine Learning Models\25 - Demo- Data Cleaning (Erroneous Data).mp4
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04 - Preparing Input Data for Machine Learning Models\26 - Demo- Data Cleaning (Outliers).mp4
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04 - Preparing Input Data for Machine Learning Models\27 - Demo- Data Cleaning (Duplicate Rows).mp4
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04 - Preparing Input Data for Machine Learning Models\28 - Demo- Data Transformation.mp4
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04 - Preparing Input Data for Machine Learning Models\29 - Demo- Reducing Data (Record Sampling).mp4
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04 - Preparing Input Data for Machine Learning Models\30 - Demo- Reducing Data (Attribute Sampling).mp4
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04 - Preparing Input Data for Machine Learning Models\31 - Demo- Discretizing Data.mp4
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05 - Handling Missing Data\32 - Introduction.mp4
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05 - Handling Missing Data\33 - Reasons Why Data Is Missing.mp4
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05 - Handling Missing Data\34 - Demo- Listwise Deletion.mp4
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05 - Handling Missing Data\35 - Problems in Deleting Rows.mp4
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05 - Handling Missing Data\36 - Demo- Using Indicator Variables.mp4
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05 - Handling Missing Data\37 - Replace with Mean, Median, and Mode.mp4
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05 - Handling Missing Data\38 - Disadvantages of Single Imputation Methods.mp4
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|
3,240,156 |
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05 - Handling Missing Data\39 - Demo- Replace with MICE.mp4
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05 - Handling Missing Data\40 - How MICE Works.mp4
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05 - Handling Missing Data\41 - Summary.mp4
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06 - Role of Feature Engineering in Machine Learning\42 - Introduction.mp4
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06 - Role of Feature Engineering in Machine Learning\43 - Why Feature Engineering.mp4
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06 - Role of Feature Engineering in Machine Learning\44 - Role of Feature Engineering in Model Complexity.mp4
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06 - Role of Feature Engineering in Machine Learning\45 - Build Better Models with Feature Engineering.mp4
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06 - Role of Feature Engineering in Machine Learning\46 - Feature Engineering Numeric Variables.mp4
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06 - Role of Feature Engineering in Machine Learning\47 - Feature Engineering Categorical Variables.mp4
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06 - Role of Feature Engineering in Machine Learning\48 - Demo- One-hot Encoding Categorical Variables.mp4
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06 - Role of Feature Engineering in Machine Learning\49 - Demo- Learning with Counts Categorical Variables.mp4
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06 - Role of Feature Engineering in Machine Learning\50 - Summary.mp4
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07 - Split a Data Set into Training and Testing Subsets\51 - Introduction.mp4
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07 - Split a Data Set into Training and Testing Subsets\52 - Demo- Training and Testing on Same Data.mp4
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07 - Split a Data Set into Training and Testing Subsets\53 - Demo- Split Data into Training and Test Set.mp4
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07 - Split a Data Set into Training and Testing Subsets\54 - Splitting Data for Model Tuning.mp4
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07 - Split a Data Set into Training and Testing Subsets\55 - Demo- Cross-validation.mp4
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07 - Split a Data Set into Training and Testing Subsets\56 - Demo- Model Selection.mp4
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07 - Split a Data Set into Training and Testing Subsets\57 - Leave-one-out Cross Validation.mp4
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07 - Split a Data Set into Training and Testing Subsets\58 - Summary.mp4
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08 - Identify Data-level Issues In Machine Learning Models\59 - Introduction.mp4
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08 - Identify Data-level Issues In Machine Learning Models\60 - Imbalanced Dataset for Classification Problems.mp4
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08 - Identify Data-level Issues In Machine Learning Models\61 - Demo- SMOTE.mp4
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08 - Identify Data-level Issues In Machine Learning Models\62 - Data Scale Issues in Distance-based Models.mp4
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08 - Identify Data-level Issues In Machine Learning Models\63 - Multicollinearity Problem in Regression Models.mp4
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08 - Identify Data-level Issues In Machine Learning Models\64 - Outliers in Regression Models.mp4
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08 - Identify Data-level Issues In Machine Learning Models\65 - Problem with High-dimensional Datasets.mp4
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08 - Identify Data-level Issues In Machine Learning Models\66 - Summary.mp4
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preparing-data-machine-learning.zip |
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02 - Getting Started with Azure Machine Learning |
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