Download Python Machine Learning Bootcamp Torrent

Torrent Contents Size: 8.63 GB

Python Machine Learning Bootcamp

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TutsNode.com.txt

TXT

63 B

[TGx]Downloaded from torrentgalaxy.to .txt

TXT

585 B

[TutsNode.com] - Python Machine Learning Bootcamp

01 Pre-Machine Learning Steps

001 Setup & Installation.en.srt	SRT	14.74 KB
001 Setup & Installation.mp4	MP4	79.19 MB
002 Loading Datasets.en.srt	SRT	12.7 KB
002 Loading Datasets.mp4	MP4	15.82 MB
003 Data Format.en.srt	SRT	11.82 KB
003 Data Format.mp4	MP4	35.45 MB
004 Train Test Splitting.en.srt	SRT	20.58 KB
004 Train Test Splitting.mp4	MP4	80.63 MB
005 Stratified Splitting.en.srt	SRT	19.39 KB
005 Stratified Splitting.mp4	MP4	86.67 MB
006 Data Preparation and Exploration.en.srt	SRT	34.58 KB
006 Data Preparation and Exploration.mp4	MP4	185.1 MB

02 Machine Learning Workflow

001 Supervised Learning Intro.en.srt	SRT	10.54 KB
001 Supervised Learning Intro.mp4	MP4	9.41 MB
002 Classification Intro.en.srt	SRT	10.07 KB
002 Classification Intro.mp4	MP4	20.48 MB
003 Logistic Regression Theory.en.srt	SRT	27.86 KB
003 Logistic Regression Theory.mp4	MP4	56.33 MB
004 Gradient Descent.en.srt	SRT	16.86 KB
004 Gradient Descent.mp4	MP4	37.93 MB
005 Types of Classification Problems.en.srt	SRT	19.07 KB
005 Types of Classification Problems.mp4	MP4	44.07 MB
006 Creating and Training a Binary Classifier.en.srt	SRT	36.72 KB
006 Creating and Training a Binary Classifier.mp4	MP4	212.03 MB
007 Creating and Training a Multiclass Classifier.en.srt	SRT	17.45 KB
007 Creating and Training a Multiclass Classifier.mp4	MP4	99.91 MB
008 Evaluating Classifiers Theory.en.srt	SRT	15.53 KB
008 Evaluating Classifiers Theory.mp4	MP4	30.94 MB
009 Precision and Recall Theory.en.srt	SRT	25.05 KB
009 Precision and Recall Theory.mp4	MP4	53.03 MB
010 ROC, Confusion Matrix, and Support Theory.en.srt	SRT	8.55 KB
010 ROC, Confusion Matrix, and Support Theory.mp4	MP4	17.96 MB
011 MNIST Data Set Intro.en.srt	SRT	9.46 KB
011 MNIST Data Set Intro.mp4	MP4	28.59 MB
012 Evaluating Classifiers Practical.en.srt	SRT	23.55 KB
012 Evaluating Classifiers Practical.mp4	MP4	115.8 MB
013 Validation Set.en.srt	SRT	6.76 KB
013 Validation Set.mp4	MP4	33.46 MB
014 Cross-Validation.en.srt	SRT	36.91 KB
014 Cross-Validation.mp4	MP4	162.79 MB
015 Hyperparameters.en.srt	SRT	21.76 KB
015 Hyperparameters.mp4	MP4	103.18 MB
016 Regularization Theory.en.srt	SRT	25.55 KB
016 Regularization Theory.mp4	MP4	60.57 MB
017 Generalization Error Sources.en.srt	SRT	20.1 KB
017 Generalization Error Sources.mp4	MP4	18.05 MB
018 Regularization Practical.en.srt	SRT	13.29 KB
018 Regularization Practical.mp4	MP4	75.55 MB
019 Grid and Randomized Search.en.srt	SRT	46.53 KB
019 Grid and Randomized Search.mp4	MP4	283.66 MB
020 Handling Missing Values.en.srt	SRT	46.92 KB
020 Handling Missing Values.mp4	MP4	255.98 MB
021 Feature Scaling Theory.en.srt	SRT	31.18 KB
021 Feature Scaling Theory.mp4	MP4	62.3 MB
022 Feature Scaling Practical.en.srt	SRT	31.6 KB
022 Feature Scaling Practical.mp4	MP4	162.01 MB
023 Text and Categorical Data.en.srt	SRT	51.23 KB
023 Text and Categorical Data.mp4	MP4	301.01 MB
024 Transformation Pipelines.en.srt	SRT	21.86 KB
024 Transformation Pipelines.mp4	MP4	97.62 MB
025 Custom Transformers.en.srt	SRT	14.37 KB
025 Custom Transformers.mp4	MP4	21.7 MB
026 Column Specific Pipelines.en.srt	SRT	21.66 KB
026 Column Specific Pipelines.mp4	MP4	122.59 MB
027 Over and Undersampling.en.srt	SRT	62.5 KB
027 Over and Undersampling.mp4	MP4	279.74 MB
028 Feature Importance.en.srt	SRT	47.84 KB
028 Feature Importance.mp4	MP4	224.44 MB
029 Saving and Loading Models and Pipelines.en.srt	SRT	22.56 KB
029 Saving and Loading Models and Pipelines.mp4	MP4	113.84 MB
030 Post Prototyping.en.srt	SRT	35.06 KB
030 Post Prototyping.mp4	MP4	86.18 MB
031 Multilabel Classification.en.srt	SRT	24.5 KB
031 Multilabel Classification.mp4	MP4	152.98 MB
032 Polynomial Features.en.srt	SRT	21.92 KB
032 Polynomial Features.mp4	MP4	97.64 MB
033 SVM Theory.en.srt	SRT	59.06 KB
033 SVM Theory.mp4	MP4	141.35 MB
034 SVM Classification Practical.en.srt	SRT	47.11 KB
034 SVM Classification Practical.mp4	MP4	266.01 MB
035 KNN Classification Theory.en.srt	SRT	26.2 KB
035 KNN Classification Theory.mp4	MP4	58.81 MB
036 KNN Classification Practical.en.srt	SRT	21.74 KB
036 KNN Classification Practical.mp4	MP4	111.56 MB
037 Decision Tree Classifier Theory.en.srt	SRT	49.33 KB
037 Decision Tree Classifier Theory.mp4	MP4	163.5 MB
038 Decision Tree Pruning.en.srt	SRT	5.03 KB
038 Decision Tree Pruning.mp4	MP4	5.78 MB
039 Decision Tree Practical.en.srt	SRT	22.3 KB
039 Decision Tree Practical.mp4	MP4	108.49 MB
040 Random Forest Theory.en.srt	SRT	12.57 KB
040 Random Forest Theory.mp4	MP4	53.9 MB
041 Random Forest Practical.en.srt	SRT	13.85 KB
041 Random Forest Practical.mp4	MP4	65.82 MB
042 Naive Bayes Theory.en.srt	SRT	13.11 KB
042 Naive Bayes Theory.mp4	MP4	26.35 MB
043 Naive Bayes Practical.en.srt	SRT	12.45 KB
043 Naive Bayes Practical.mp4	MP4	63.73 MB
044 How to Choose a Model.en.srt	SRT	17.18 KB
044 How to Choose a Model.mp4	MP4	46.08 MB

03 Regression

001 Regression Intro.en.srt	SRT	25.1 KB
001 Regression Intro.mp4	MP4	22.98 MB
002 Linear Regression Practical.en.srt	SRT	29.49 KB
002 Linear Regression Practical.mp4	MP4	142.48 MB
003 Regularized Linear Regression Practical.en.srt	SRT	33.74 KB
003 Regularized Linear Regression Practical.mp4	MP4	140.67 MB
004 Boston Housing Intro.en.srt	SRT	27.22 KB
004 Boston Housing Intro.mp4	MP4	166.06 MB
005 Polynomial Regression.en.srt	SRT	26.33 KB
005 Polynomial Regression.mp4	MP4	121.95 MB
006 Regression Losses and Learning Rates.en.srt	SRT	17.38 KB
006 Regression Losses and Learning Rates.mp4	MP4	14.36 MB
007 SGD Regression.en.srt	SRT	32.14 KB
007 SGD Regression.mp4	MP4	177.57 MB
008 KNN Regression Theory.en.srt	SRT	4.71 KB
008 KNN Regression Theory.mp4	MP4	12.03 MB
009 KNN Regression Practical.en.srt	SRT	11.83 KB
009 KNN Regression Practical.mp4	MP4	83.25 MB
010 SVM Regression Theory.en.srt	SRT	7.63 KB
010 SVM Regression Theory.mp4	MP4	21.69 MB
011 SVM Regression Practical.en.srt	SRT	13.41 KB
011 SVM Regression Practical.mp4	MP4	85.83 MB
012 Decision Tree Regression Theory.en.srt	SRT	9.61 KB
012 Decision Tree Regression Theory.mp4	MP4	20.54 MB
013 Decision Tree and Random Forest Regression Practical.en.srt	SRT	17.3 KB
013 Decision Tree and Random Forest Regression Practical.mp4	MP4	101.25 MB
014 Additional Regression Metrics.en.srt	SRT	10.41 KB
014 Additional Regression Metrics.mp4	MP4	16.82 MB
015 Ensembles Intro.en.srt	SRT	8.47 KB
015 Ensembles Intro.mp4	MP4	17.75 MB
016 Voting Ensembles Theory.en.srt	SRT	9.89 KB
016 Voting Ensembles Theory.mp4	MP4	19.95 MB
017 Voting Classification Practical.en.srt	SRT	18.73 KB
017 Voting Classification Practical.mp4	MP4	89.45 MB
018 Voting Regression Practical.en.srt	SRT	6.22 KB
018 Voting Regression Practical.mp4	MP4	30.41 MB
019 Bagging and Pasting Theory.en.srt	SRT	14.38 KB
019 Bagging and Pasting Theory.mp4	MP4	33.06 MB
020 Bagging and Pasting Classification Practical.en.srt	SRT	14.21 KB
020 Bagging and Pasting Classification Practical.mp4	MP4	71.07 MB
021 Bagging and Pasting Regression Practical.en.srt	SRT	11.6 KB
021 Bagging and Pasting Regression Practical.mp4	MP4	60.35 MB
022 AdaBoost Theory.en.srt	SRT	16.55 KB
022 AdaBoost Theory.mp4	MP4	35.04 MB
023 AdaBoost Classification Practical.en.srt	SRT	18.5 KB
023 AdaBoost Classification Practical.mp4	MP4	112.87 MB
024 AdaBoost Regression Practical.en.srt	SRT	6.6 KB
024 AdaBoost Regression Practical.mp4	MP4	35.16 MB
025 Gradient Boosting Theory.en.srt	SRT	18.64 KB
025 Gradient Boosting Theory.mp4	MP4	45.71 MB
026 Gradient Boosting Classification Pratical.en.srt	SRT	13.3 KB
026 Gradient Boosting Classification Pratical.mp4	MP4	73.77 MB
027 Gradient Boosting Regression Practical.en.srt	SRT	10.44 KB
027 Gradient Boosting Regression Practical.mp4	MP4	57.8 MB
028 Stacking and Blending Theory.en.srt	SRT	11.38 KB
028 Stacking and Blending Theory.mp4	MP4	26.18 MB
029 Stacking Classifiers Practical.en.srt	SRT	20.19 KB
029 Stacking Classifiers Practical.mp4	MP4	107.77 MB
030 Stacking Regression Practical.en.srt	SRT	10.41 KB
030 Stacking Regression Practical.mp4	MP4	58.21 MB
031 Dimensionality Reduction Intro.en.srt	SRT	25.38 KB
031 Dimensionality Reduction Intro.mp4	MP4	80.37 MB
032 PCA Theory.en.srt	SRT	43.72 KB
032 PCA Theory.mp4	MP4	114.42 MB
033 PCA Practical.en.srt	SRT	42.17 KB
033 PCA Practical.mp4	MP4	220.99 MB
034 NNMF Theory.en.srt	SRT	10.68 KB
034 NNMF Theory.mp4	MP4	21.48 MB
035 NNMF Practical.en.srt	SRT	23.78 KB
035 NNMF Practical.mp4	MP4	114.14 MB
036 Isomap Theory.en.srt	SRT	7 KB
036 Isomap Theory.mp4	MP4	14.18 MB
037 Isomap Practical.en.srt	SRT	24.86 KB
037 Isomap Practical.mp4	MP4	120.87 MB
038 LLE Theory.en.srt	SRT	16.17 KB
038 LLE Theory.mp4	MP4	39.73 MB
039 LLE Practical.en.srt	SRT	18.84 KB
039 LLE Practical.mp4	MP4	111.13 MB
040 t-SNE Theory.en.srt	SRT	24.85 KB
040 t-SNE Theory.mp4	MP4	66.26 MB
041 t-SNE Practical.en.srt	SRT	21.76 KB
041 t-SNE Practical.mp4	MP4	113.48 MB
042 Unsupervised Learning Intro.en.srt	SRT	10.97 KB
042 Unsupervised Learning Intro.mp4	MP4	19.28 MB
043 KMeans Theory.en.srt	SRT	16.43 KB
043 KMeans Theory.mp4	MP4	31.67 MB
044 KMeans Practical.en.srt	SRT	25.36 KB
044 KMeans Practical.mp4	MP4	126.27 MB
045 Choosing Number of Clusters Theory.en.srt	SRT	29.88 KB
045 Choosing Number of Clusters Theory.mp4	MP4	64.35 MB
046 Choosing Number of Clusters Practical.en.srt	SRT	16.46 KB
046 Choosing Number of Clusters Practical.mp4	MP4	112.91 MB
047 DBSCAN Theory.en.srt	SRT	15.61 KB
047 DBSCAN Theory.mp4	MP4	33.55 MB
048 DBSCAN Practical.en.srt	SRT	14.8 KB
048 DBSCAN Practical.mp4	MP4	61.4 MB
049 Gaussian Mixture Theory.en.srt	SRT	23.74 KB
049 Gaussian Mixture Theory.mp4	MP4	48.92 MB
050 Gaussian Mixture Practical.en.srt	SRT	23.69 KB
050 Gaussian Mixture Practical.mp4	MP4	126.4 MB
051 Semi-Supervised Theory.en.srt	SRT	16.91 KB
051 Semi-Supervised Theory.mp4	MP4	14.83 MB
052 Semi-Supervised Practical.en.srt	SRT	19.15 KB
052 Semi-Supervised Practical.mp4	MP4	96.44 MB

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Python Machine Learning Bootcamp

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