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Master Machine Learning in Python with Scikit-Learn
Language: English | Size:3.3 GB
Genre:eLearning

Files Included :

1  Introduction to the Course.mp4 (14.26 MB)
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3  (Background) Introduction to Jupyter Notebooks.mp4 (48.55 MB)
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4  (Background) Introduction to NumPy.mp4 (66.51 MB)
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5  (Background) Introduction to Pandas.mp4 (70.09 MB)
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1  Introduction.mp4 (5.94 MB)
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11  One Hot Encoding and Pipelines.mp4 (46.79 MB)
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12  Cross-Validation and Pipelines.mp4 (35.85 MB)
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2  One Hot Encoding.mp4 (11.68 MB)
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4  Using One Hot Encoding.mp4 (55.56 MB)
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6  Cross-Validation.mp4 (8.34 MB)
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7  Using Cross-Validation.mp4 (41.37 MB)
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9  Validation and Test Set.mp4 (9.45 MB)
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1  Introduction.mp4 (4.56 MB)
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2  Regularization (or Shrinkage).mp4 (16.68 MB)
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4  Lasso and Ridge Regression.mp4 (48.23 MB)
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6  Bias-Variance Tradeoff.mp4 (27.7 MB)
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8  Finding a Good Parameter Value.mp4 (47.37 MB)
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1  Introduction.mp4 (2.51 MB)
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2  Support Vector Machine.mp4 (10.22 MB)
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4  Implementing SVM.mp4 (29.46 MB)
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5  Hyperparameters.mp4 (30.58 MB)
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7  Implementing Grid Search.mp4 (48.72 MB)
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1  Introduction.mp4 (18.07 MB)
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2  Solution Classification Project.mp4 (165.17 MB)
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1  Introduction.mp4 (4.97 MB)
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10  Implementing PCA.mp4 (24.25 MB)
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2  Dimensionality Reduction.mp4 (30.4 MB)
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4  Introducing the CovType Dataset.mp4 (48.71 MB)
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5  Reduction Based on Correlation.mp4 (55.72 MB)
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6  Reduction Based on Variance.mp4 (55.71 MB)
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8  Principal Component Analysis (PCA).mp4 (29.61 MB)
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1  Introduction.mp4 (3.23 MB)
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2  K-Nearest Neighbors.mp4 (10.42 MB)
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4  Implementing KNN.mp4 (52.22 MB)
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6  Model Persistence.mp4 (11.56 MB)
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7  Using Model Persistence.mp4 (25.38 MB)
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1  Introduction.mp4 (4.17 MB)
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2  What are Neural Networks.mp4 (19.86 MB)
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4  Weights and Activation Functions.mp4 (35.29 MB)
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6  Basic Usage of MLPClassifier.mp4 (31.4 MB)
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7  Parameters and Keras.mp4 (28.93 MB)
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1  Introduction.mp4 (4.16 MB)
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2  What is Unsupervised Learning.mp4 (36.37 MB)
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4  K-Means Clustering.mp4 (19.9 MB)
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5  Implementing K-Means Clustering.mp4 (83.71 MB)
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1  Introduction.mp4 (14.64 MB)
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2  Solution Unsupervised Project.mp4 (146.37 MB)
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1  The End of Our Journey.mp4 (3.35 MB)
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1  What is Machine Learning.mp4 (26.33 MB)
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2  ML Terminology.mp4 (34.4 MB)
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4  Anatomy of a ML Project.mp4 (42.49 MB)
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6  Introducing Scikit Learn.mp4 (67.46 MB)
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8  Exploring the Diabetes Dataset.mp4 (72.21 MB)
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1  Introduction.mp4 (6 MB)
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12  How is Training Done (Optional Theory).mp4 (95.73 MB)
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2  Idea of Linear Regression.mp4 (8.16 MB)
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3  The Theory of Linear Regression.mp4 (21.12 MB)
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5  Linear Regression in Scikit-Learn.mp4 (24.18 MB)
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7  Evaluating the Model.mp4 (16.76 MB)
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9  Is our Model any Good.mp4 (54.64 MB)
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1  Introduction.mp4 (6.23 MB)
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2  Binary Classification and Logistic Regression.mp4 (31.49 MB)
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4  The Iris Dataset.mp4 (69 MB)
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5  Implementing Logistic Regression.mp4 (38.45 MB)
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7  Accuracy Score.mp4 (24.49 MB)
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1  Introduction.mp4 (3.53 MB)
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2  Preprocessing.mp4 (28.17 MB)
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4  Filling in Missing Values.mp4 (83.87 MB)
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6  Choosing Relevant Features.mp4 (63.84 MB)
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7  Standard Scaling in Scikit-Learn.mp4 (67.84 MB)
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9  Pipelines.mp4 (42.94 MB)
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1  Introduction.mp4 (6.53 MB)
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10  Fitting Everything Into a Pipeline.mp4 (42.44 MB)
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11  Overfitting and Underfitting.mp4 (22.41 MB)
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13  Overfitting in Practice.mp4 (45.75 MB)
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2  Understanding Polynomial Regression.mp4 (35.22 MB)
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4  Adding Polynomial Features Manually.mp4 (55.95 MB)
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6  Evaluating with Mean Absolute Error.mp4 (58.22 MB)
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8  Using the Polynomial Features Class.mp4 (48.06 MB)
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1  Introduction.mp4 (10.19 MB)
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2  Solution Regression Project.mp4 (106.63 MB)
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1  Introduction.mp4 (3.23 MB)
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10  Using Precision and Recall.mp4 (45.81 MB)
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2  Introduction to Trees.mp4 (14.44 MB)
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3  Decision Trees.mp4 (19.75 MB)
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5  Implementing Decision Trees.mp4 (46.19 MB)
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7  False Positives and False Negatives.mp4 (20.73 MB)
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8  Understanding Precision and Recall.mp4 (22.23 MB)
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1  Introduction.mp4 (3.43 MB)
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2  What is Ensemble Learning.mp4 (23.44 MB)
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4  Creating Multiple Models Fast.mp4 (38.99 MB)
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5  Creating an Ensemble Majority Vote.mp4 (50.89 MB)
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7  Weak Learners and Bagging.mp4 (27.22 MB)
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9  Using Random Forests.mp4 (54.79 MB)
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