O'Reilly - Advanced Machine Learning with scikit learn

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Added on November 2, 2015 by nex_xenin Other > Tutorials
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O'Reilly - Advanced Machine Learning with scikit learn (Size: 765.84 MB)

		01_01-What To Expect And About The Author.mp4	12.51 MB
		01_02-Setup.mp4	5.86 MB
		01_03-The Classifier Interface.mp4	27.18 MB
		01_04-The Regressor Interface.mp4	10.81 MB
		01_05-The Transformer Interface.mp4	7.9 MB
		01_06-The Cluster Interface.mp4	22.34 MB
		01_07-The Manifold Interface.mp4	10.96 MB
		01_08-scikitLearn Interface Summary.mp4	11.31 MB
		01_09-CrossValidation With Cross_Val_Score.mp4	20.32 MB
		01_10-Parameter Searches With GridSearchCV.mp4	19.34 MB
		01_11-How To Access Your Working Files.mp4	26.38 MB
		02_01-What Is Model Complexity And Overfitting.mp4	7.25 MB
		02_02-Linear Models InDepth.mp4	33.58 MB
		02_03-Kernel SVMs InDepth.mp4	21.89 MB
		02_04-Random Forests InDepth.mp4	15.13 MB
		02_05-Learning Curves For Analyzing Model Complexity.mp4	12.64 MB
		02_06-Validation Curves For Analyzing Model Parameters.mp4	7.61 MB
		02_07-Efficient Parameter Search With EstimatorCV Objects.mp4	18.08 MB
		03_01-Motivation Of Using Pipelines.mp4	9.69 MB
		03_02-Defining A Pipeline And Basic Usage.mp4	19.08 MB
		03_03-CrossValidation With Pipelines.mp4	7.76 MB
		03_04-Parameter Selection With Pipelines.mp4	16.74 MB
		04_01-Be Mindful Of Default Metrics.mp4	20.6 MB
		04_02-More Evaluation Methods For Classification.mp4	14.57 MB
		04_03-AUC.mp4	19.69 MB
		04_04-Defining Custom Metrics.mp4	20.44 MB
		05_01-Guidelines For Unsupervised Model Selection.mp4	21.63 MB
		05_02-Model Selection For Density Models.mp4	18.17 MB
		05_03-Model Selection For Clustering.mp4	14.02 MB
		06_01-Why Real Data Is Messy.mp4	19.04 MB
		06_02-OneHot Encoding For Categorical Data.mp4	18.12 MB
		06_03-Working With Dictionaries.mp4	6.42 MB
		06_04-Handling Incomplete Data.mp4	14.68 MB
		07_01-Motivation.mp4	8.03 MB
		07_02-BagOfWords Representations.mp4	19.1 MB
		07_03-Text Classification For Sentiment Analysis Part 1.mp4	25.36 MB
		07_04-Text Classification For Sentiment Analysis Part 2.mp4	12.97 MB
		07_05-The Hashing Trick.mp4	9.11 MB
		07_06-Other Representations Distributed Word Representations.mp4	5.08 MB
		08_01-The TradeOffs Of Out Of Core Learning.mp4	10.8 MB
		08_02-The scikitLearn Interface For Out Of Core Learning.mp4	14.67 MB
		08_03-Kernel Approximations For LargeScale NonLinear Classification.mp4	16.04 MB
		08_04-Subsample And Transform Supervised Transformations For Out Of Core Learning.mp4	18.35 MB
		08_05-Application OutOfCore Text Classification.mp4	18.4 MB
		09_01-summary.mp4	10.07 MB
		09_02-Where To Go From Here.mp4	8.62 MB
		Advanced_Machine_Learning_with_scikit_learn_Working_Files.zip	57.48 MB

Description

In this Advanced Machine Learning with scikit-learn training course, expert author Andreas Mueller will teach you how to choose and evaluate machine learning models. This course is designed for users that already have experience with Python.
You will start by learning about model complexity, overfitting and underfitting. From there, Andreas will teach you about pipelines, advanced metrics and imbalanced classes, and model selection for unsupervised learning. This video tutorial also covers dealing with categorical variables, dictionaries, and incomplete data, and how to handle text data. Finally, you will learn about out of core learning, including the sci-learn interface for out of core learning and kernel approximations for large-scale non-linear classification.
Once you have completed this computer based training course, you will have learned everything you need to know to be able to choose and evaluate machine learning models. Working files are included, allowing you to follow along with the author throughout the lessons.

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765.84 MB

seeders:22

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O'Reilly - Advanced Machine Learning with scikit learn

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viru338

thank u very much

pitar

Thanks a lot!