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| Archived
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preparing-data-modeling-scikit-learn.zip |
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4 - Preparing Text Data for Machine Learning\29 - Bag-of-words and Bag-of-n-grams Models.mp4
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4 - Preparing Text Data for Machine Learning\28 - Representing Text Data in Numeric Form.mp4
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4 - Preparing Text Data for Machine Learning\34 - Reducing Dimensions Using the Hashing Vectorizer.mp4
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4 - Preparing Text Data for Machine Learning\33 - Hashing for Dimensionality Reduction.mp4
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4 - Preparing Text Data for Machine Learning\35 - Performing Feature Extraction on a Python Dictionary .mp4
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4 - Preparing Text Data for Machine Learning\30 - Vectorize Text Using the Bag-of-words Model.mp4
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4 - Preparing Text Data for Machine Learning\31 - Vectorize Text Using the Bag-of-n-grams Model.mp4
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4 - Preparing Text Data for Machine Learning\36 - Module Summary.mp4
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4 - Preparing Text Data for Machine Learning\32 - Vectorize Text Using Tf-Idf Scores.mp4
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4 - Preparing Text Data for Machine Learning\27 - Module Overview.mp4
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1 - Course Overview\01 - Course Overview.mp4
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2 - Preparing Numeric Data for Machine Learning\02 - Module Overview.mp4
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2 - Preparing Numeric Data for Machine Learning\10 - Normalization and Cosine Similarity.mp4
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2 - Preparing Numeric Data for Machine Learning\06 - Transforming Data to Gaussian Distributions.mp4
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2 - Preparing Numeric Data for Machine Learning\12 - Reducing Dimensionality Using Factor Analysis.mp4
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2 - Preparing Numeric Data for Machine Learning\07 - Calculating and Visualizing Summary Statistics.mp4
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2 - Preparing Numeric Data for Machine Learning\13 - Module Summary.mp4
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2 - Preparing Numeric Data for Machine Learning\05 - Normalization.mp4
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2 - Preparing Numeric Data for Machine Learning\08 - Using the Standard Scaler for Standardizing Numeric Features.mp4
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2 - Preparing Numeric Data for Machine Learning\11 - Transforming Bimodally Distributed Data to a Normal Distribution Using a Quantile Transformer.mp4
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2 - Preparing Numeric Data for Machine Learning\09 - Using the Robust Scaler to Scale Numeric Features.mp4
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2 - Preparing Numeric Data for Machine Learning\04 - Scaling and Standardization.mp4
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2 - Preparing Numeric Data for Machine Learning\03 - Prerequisites and Course Outline.mp4
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7 - Performing Kernel Approximations \54 - Preparing Image Data.mp4
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7 - Performing Kernel Approximations \55 - Comparing Classifiers Trained Using Implicit and Explict Features.mp4
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7 - Performing Kernel Approximations \53 - Kernel Approximations.mp4
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7 - Performing Kernel Approximations \57 - Summary and Further Study.mp4
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7 - Performing Kernel Approximations \56 - Comparing Accuracy and Runtime for Different Sample Sizes.mp4
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7 - Performing Kernel Approximations \52 - Support Vector Classifiers and the Kernel Trick.mp4
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7 - Performing Kernel Approximations \51 - Module Overview.mp4
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5 - Preparing Image Data for Machine Learning\40 - Extracting Patches from Image Data.mp4
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5 - Preparing Image Data for Machine Learning\37 - Module Overview.mp4
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5 - Preparing Image Data for Machine Learning\42 - Clustering Image Data Using a Pixel Connectivity Graph.mp4
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5 - Preparing Image Data for Machine Learning\39 - Feature Extraction from Images.mp4
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5 - Preparing Image Data for Machine Learning\38 - Representing Images as Matrices.mp4
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5 - Preparing Image Data for Machine Learning\43 - Clustering Images Using a Gradient Connectivity Graph.mp4
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5 - Preparing Image Data for Machine Learning\41 - Using Dictionary Learning to Denoise and Reconstruct Images.mp4
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5 - Preparing Image Data for Machine Learning\44 - Module Summary.mp4
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6 - Working with Specialized Datasets\49 - Generating Manifold Data.mp4
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6 - Working with Specialized Datasets\47 - Exploring Internal Datasets.mp4
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6 - Working with Specialized Datasets\48 - Creating Artificial Datasets for Regression, Classification, Clustering, and Dimensionality Reduction.mp4
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6 - Working with Specialized Datasets\46 - Internal, Artificial, and External Datasets in Scikit Learn.mp4
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6 - Working with Specialized Datasets\50 - Module Summary.mp4
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6 - Working with Specialized Datasets\45 - Module Overview.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \14 - Module Overview.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \21 - Outlier Detection Using Isolation Forest.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \22 - Outlier Detection Using Elliptic Envelope.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \24 - Using the Predict Score Samples and Decision Function.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \20 - Outlier Detection Using Local Outlier Factor.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \15 - Outliers and Novelties.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \23 - Novelty Detection Using Local Outlier Factor.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \17 - Local Outlier Factor.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \18 - Elliptic Envelope.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \19 - Isolation Forest.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \25 - Outlier Detection Using the Head Brain Dataset.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \16 - Detecting and Coping with Outlier Data.mp4
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3 - Understanding and Implementing Novelty and Outlier Detection \26 - Module Summary.mp4
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4 - Preparing Text Data for Machine Learning |
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