Master Machine Learning in Python with Scikit-Learn

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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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