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Python Machine Learning Bootcamp
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[TutsNode.com] - Python Machine Learning Bootcamp (Size: 8.63 GB) (Files: 307)
[TutsNode.com] - Python Machine Learning Bootcamp
02 Machine Learning Workflow
023 Text and Categorical Data.mp4
027 Over and Undersampling.en.srt
033 SVM Theory.en.srt
023 Text and Categorical Data.en.srt
037 Decision Tree Classifier Theory.en.srt
028 Feature Importance.en.srt
034 SVM Classification Practical.en.srt
020 Handling Missing Values.en.srt
019 Grid and Randomized Search.en.srt
014 Cross-Validation.en.srt
006 Creating and Training a Binary Classifier.en.srt
030 Post Prototyping.en.srt
022 Feature Scaling Practical.en.srt
021 Feature Scaling Theory.en.srt
003 Logistic Regression Theory.en.srt
035 KNN Classification Theory.en.srt
016 Regularization Theory.en.srt
019 Grid and Randomized Search.mp4
009 Precision and Recall Theory.en.srt
031 Multilabel Classification.en.srt
012 Evaluating Classifiers Practical.en.srt
029 Saving and Loading Models and Pipelines.en.srt
039 Decision Tree Practical.en.srt
032 Polynomial Features.en.srt
024 Transformation Pipelines.en.srt
015 Hyperparameters.en.srt
027 Over and Undersampling.mp4
036 KNN Classification Practical.en.srt
026 Column Specific Pipelines.en.srt
017 Generalization Error Sources.en.srt
005 Types of Classification Problems.en.srt
007 Creating and Training a Multiclass Classifier.en.srt
034 SVM Classification Practical.mp4
044 How to Choose a Model.en.srt
004 Gradient Descent.en.srt
008 Evaluating Classifiers Theory.en.srt
025 Custom Transformers.en.srt
041 Random Forest Practical.en.srt
018 Regularization Practical.en.srt
042 Naive Bayes Theory.en.srt
040 Random Forest Theory.en.srt
043 Naive Bayes Practical.en.srt
001 Supervised Learning Intro.en.srt
002 Classification Intro.en.srt
011 MNIST Data Set Intro.en.srt
010 ROC, Confusion Matrix, and Support Theory.en.srt
013 Validation Set.en.srt
038 Decision Tree Pruning.en.srt
020 Handling Missing Values.mp4
028 Feature Importance.mp4
006 Creating and Training a Binary Classifier.mp4
037 Decision Tree Classifier Theory.mp4
014 Cross-Validation.mp4
022 Feature Scaling Practical.mp4
031 Multilabel Classification.mp4
033 SVM Theory.mp4
026 Column Specific Pipelines.mp4
012 Evaluating Classifiers Practical.mp4
029 Saving and Loading Models and Pipelines.mp4
036 KNN Classification Practical.mp4
039 Decision Tree Practical.mp4
015 Hyperparameters.mp4
007 Creating and Training a Multiclass Classifier.mp4
032 Polynomial Features.mp4
024 Transformation Pipelines.mp4
030 Post Prototyping.mp4
018 Regularization Practical.mp4
041 Random Forest Practical.mp4
043 Naive Bayes Practical.mp4
021 Feature Scaling Theory.mp4
016 Regularization Theory.mp4
035 KNN Classification Theory.mp4
003 Logistic Regression Theory.mp4
040 Random Forest Theory.mp4
009 Precision and Recall Theory.mp4
044 How to Choose a Model.mp4
005 Types of Classification Problems.mp4
004 Gradient Descent.mp4
013 Validation Set.mp4
008 Evaluating Classifiers Theory.mp4
011 MNIST Data Set Intro.mp4
042 Naive Bayes Theory.mp4
025 Custom Transformers.mp4
002 Classification Intro.mp4
017 Generalization Error Sources.mp4
010 ROC, Confusion Matrix, and Support Theory.mp4
001 Supervised Learning Intro.mp4
038 Decision Tree Pruning.mp4
03 Regression
032 PCA Theory.en.srt
033 PCA Practical.en.srt
003 Regularized Linear Regression Practical.en.srt
007 SGD Regression.en.srt
045 Choosing Number of Clusters Theory.en.srt
002 Linear Regression Practical.en.srt
004 Boston Housing Intro.en.srt
005 Polynomial Regression.en.srt
031 Dimensionality Reduction Intro.en.srt
044 KMeans Practical.en.srt
001 Regression Intro.en.srt
037 Isomap Practical.en.srt
040 t-SNE Theory.en.srt
035 NNMF Practical.en.srt
049 Gaussian Mixture Theory.en.srt
050 Gaussian Mixture Practical.en.srt
041 t-SNE Practical.en.srt
029 Stacking Classifiers Practical.en.srt
052 Semi-Supervised Practical.en.srt
039 LLE Practical.en.srt
017 Voting Classification Practical.en.srt
025 Gradient Boosting Theory.en.srt
023 AdaBoost Classification Practical.en.srt
048 DBSCAN Practical.en.srt
006 Regression Losses and Learning Rates.en.srt
013 Decision Tree and Random Forest Regression Practical.en.srt
051 Semi-Supervised Theory.en.srt
022 AdaBoost Theory.en.srt
046 Choosing Number of Clusters Practical.en.srt
043 KMeans Theory.en.srt
038 LLE Theory.en.srt
047 DBSCAN Theory.en.srt
019 Bagging and Pasting Theory.en.srt
020 Bagging and Pasting Classification Practical.en.srt
011 SVM Regression Practical.en.srt
026 Gradient Boosting Classification Pratical.en.srt
009 KNN Regression Practical.en.srt
021 Bagging and Pasting Regression Practical.en.srt
028 Stacking and Blending Theory.en.srt
042 Unsupervised Learning Intro.en.srt
034 NNMF Theory.en.srt
027 Gradient Boosting Regression Practical.en.srt
030 Stacking Regression Practical.en.srt
014 Additional Regression Metrics.en.srt
016 Voting Ensembles Theory.en.srt
012 Decision Tree Regression Theory.en.srt
015 Ensembles Intro.en.srt
010 SVM Regression Theory.en.srt
036 Isomap Theory.en.srt
024 AdaBoost Regression Practical.en.srt
018 Voting Regression Practical.en.srt
008 KNN Regression Theory.en.srt
033 PCA Practical.mp4
007 SGD Regression.mp4
004 Boston Housing Intro.mp4
002 Linear Regression Practical.mp4
003 Regularized Linear Regression Practical.mp4
050 Gaussian Mixture Practical.mp4
044 KMeans Practical.mp4
005 Polynomial Regression.mp4
037 Isomap Practical.mp4
032 PCA Theory.mp4
035 NNMF Practical.mp4
041 t-SNE Practical.mp4
046 Choosing Number of Clusters Practical.mp4
023 AdaBoost Classification Practical.mp4
039 LLE Practical.mp4
029 Stacking Classifiers Practical.mp4
013 Decision Tree and Random Forest Regression Practical.mp4
052 Semi-Supervised Practical.mp4
017 Voting Classification Practical.mp4
011 SVM Regression Practical.mp4
009 KNN Regression Practical.mp4
031 Dimensionality Reduction Intro.mp4
026 Gradient Boosting Classification Pratical.mp4
020 Bagging and Pasting Classification Practical.mp4
040 t-SNE Theory.mp4
045 Choosing Number of Clusters Theory.mp4
048 DBSCAN Practical.mp4
021 Bagging and Pasting Regression Practical.mp4
030 Stacking Regression Practical.mp4
027 Gradient Boosting Regression Practical.mp4
049 Gaussian Mixture Theory.mp4
025 Gradient Boosting Theory.mp4
038 LLE Theory.mp4
024 AdaBoost Regression Practical.mp4
022 AdaBoost Theory.mp4
047 DBSCAN Theory.mp4
019 Bagging and Pasting Theory.mp4
043 KMeans Theory.mp4
018 Voting Regression Practical.mp4
028 Stacking and Blending Theory.mp4
001 Regression Intro.mp4
010 SVM Regression Theory.mp4
034 NNMF Theory.mp4
012 Decision Tree Regression Theory.mp4
016 Voting Ensembles Theory.mp4
042 Unsupervised Learning Intro.mp4
015 Ensembles Intro.mp4
014 Additional Regression Metrics.mp4
051 Semi-Supervised Theory.mp4
006 Regression Losses and Learning Rates.mp4
036 Isomap Theory.mp4
008 KNN Regression Theory.mp4
01 Pre-Machine Learning Steps
006 Data Preparation and Exploration.en.srt
004 Train Test Splitting.en.srt
005 Stratified Splitting.en.srt
001 Setup & Installation.en.srt
002 Loading Datasets.en.srt
003 Data Format.en.srt
006 Data Preparation and Exploration.mp4
005 Stratified Splitting.mp4
004 Train Test Splitting.mp4
001 Setup & Installation.mp4
003 Data Format.mp4
002 Loading Datasets.mp4
TutsNode.com.txt
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