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种子名称： GetFreeCourses.Co-Udemy-The Supervised Machine Learning Course
文件类型： 视频
文件数目： 75个文件
文件大小： 2.68 GB
收录时间： 2022-10-9 11:28
已经下载： 3次
资源热度： 121
最近下载： 2024-5-10 19:38

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GetFreeCourses.Co-Udemy-The Supervised Machine Learning Course.torrent

1 - Introduction/1 - Introduction.mp469.89MB

2 - Setting up the Environment/2 - Installing Anaconda.mp429.53MB

2 - Setting up the Environment/3 - Jupyter Dashboard Part 1.mp410.25MB

2 - Setting up the Environment/4 - Jupyter Dashboard Part 2.mp420.99MB

2 - Setting up the Environment/5 - Installing the relevant packages.mp422.78MB

3 - Naïve Bayes/10 - The HamorSpam Example.mp466.89MB

3 - Naïve Bayes/12 - The YouTube Dataset Creating the Data Frame.mp433.75MB

3 - Naïve Bayes/13 - CountVectorizer.mp436.99MB

3 - Naïve Bayes/14 - The YouTube Dataset Preprocessing.mp443.97MB

3 - Naïve Bayes/16 - The YouTube Dataset Classification.mp423.67MB

3 - Naïve Bayes/18 - The YouTube Dataset Confusion Matrix.mp417.79MB

3 - Naïve Bayes/19 - The YouTube Dataset Accuracy Precision Recall and the F1 score.mp437.74MB

3 - Naïve Bayes/20 - The YouTube Dataset Changing the Priors.mp440.34MB

3 - Naïve Bayes/6 - Motivation.mp443.38MB

3 - Naïve Bayes/7 - Bayes Thought Experiment.mp433.66MB

3 - Naïve Bayes/9 - Bayes Theorem.mp465.92MB

4 - KNearest Neighbors/22 - Motivation.mp424.43MB

4 - KNearest Neighbors/23 - Math Prerequisites Distance Metrics.mp427.79MB

4 - KNearest Neighbors/24 - Random Dataset Generating the Dataset.mp416.85MB

4 - KNearest Neighbors/25 - Random Dataset Visualizing the Dataset.mp428.19MB

4 - KNearest Neighbors/26 - Random Dataset Classification.mp450.6MB

4 - KNearest Neighbors/27 - Random Dataset How to Break a Tie.mp426.76MB

4 - KNearest Neighbors/28 - Random Dataset Decision Regions.mp446MB

4 - KNearest Neighbors/29 - Random Dataset Choosing the Best Kvalue.mp432.64MB

4 - KNearest Neighbors/30 - Random Dataset Grid Search.mp427.88MB

4 - KNearest Neighbors/31 - Random Dataset Model Performance.mp421.21MB

4 - KNearest Neighbors/33 - Theory with a Practical Example.mp443.75MB

4 - KNearest Neighbors/34 - KNN vs Linear Regression A Linear Problem.mp446.73MB

4 - KNearest Neighbors/35 - KNN vs Linear Regression A Nonlinear Problem.mp433.11MB

4 - KNearest Neighbors/37 - Pros and Cons.mp455.22MB

5 - Decision Trees and Random Forests/38 - What is a Tree in Computer Science.mp434.84MB

5 - Decision Trees and Random Forests/39 - The Concept of Decision Trees.mp431.62MB

5 - Decision Trees and Random Forests/40 - Decision Trees in Machine Learning.mp447.34MB

5 - Decision Trees and Random Forests/41 - Decision Trees Pros and Cons.mp469.13MB

5 - Decision Trees and Random Forests/42 - Practical Example The Iris Dataset.mp421.83MB

5 - Decision Trees and Random Forests/43 - Practical Example Creating a Decision Tree.mp432.83MB

5 - Decision Trees and Random Forests/44 - Practical Example Plotting the Tree.mp442.97MB

5 - Decision Trees and Random Forests/45 - Decision Tree Metrics Intuition Gini Inpurity.mp453.2MB

5 - Decision Trees and Random Forests/46 - Decision Tree Metrics Information Gain.mp426.36MB

5 - Decision Trees and Random Forests/47 - Tree Pruning Dealing with Overfitting.mp439.41MB

5 - Decision Trees and Random Forests/48 - Random Forest as Ensemble Learning.mp431.99MB

5 - Decision Trees and Random Forests/49 - Bootstrapping.mp429.92MB

5 - Decision Trees and Random Forests/50 - From Bootstrapping to Random Forests.mp425.93MB

5 - Decision Trees and Random Forests/51 - Random Forest in Code Glass Dataset.mp452.81MB

5 - Decision Trees and Random Forests/52 - Census Data and Income Preprocessing.mp459.6MB

5 - Decision Trees and Random Forests/53 - Training the Decision Tree.mp427.35MB

5 - Decision Trees and Random Forests/54 - Training the Random Forest.mp427.17MB

6 - Support Vector Machines/55 - Introduction to Support Vector Machines.mp462.4MB

6 - Support Vector Machines/56 - Linearly separable classes hard margin problem.mp459.15MB

6 - Support Vector Machines/57 - Nonlinearly separable classes soft margin problem.mp449.84MB

6 - Support Vector Machines/58 - Kernels Intuition.mp477.05MB

6 - Support Vector Machines/59 - Intro to the practical case.mp431.54MB

6 - Support Vector Machines/60 - Preprocessing the data.mp415.06MB

6 - Support Vector Machines/61 - Splitting the data into train and test and rescaling.mp416.4MB

6 - Support Vector Machines/62 - Implementing a linear SVM.mp412.66MB

6 - Support Vector Machines/63 - Analyzing the results– Confusion Matrix Precision and Recall.mp428.22MB

6 - Support Vector Machines/64 - Crossvalidation.mp476.47MB

6 - Support Vector Machines/65 - Choosing the kernels and C values for crossvalidation.mp418.81MB

6 - Support Vector Machines/66 - Hyperparameter tuning using GridSearchCV.mp435.14MB

7 - Ridge and Lasso Regression/68 - Regression Analysis Overview.mp429.41MB

7 - Ridge and Lasso Regression/69 - Overfitting and Multicollinearity.mp428.15MB

7 - Ridge and Lasso Regression/70 - Introduction to Regularization.mp425.96MB

7 - Ridge and Lasso Regression/71 - Ridge Regression Basics.mp443.16MB

7 - Ridge and Lasso Regression/72 - Ridge Regression Mechanics.mp446.67MB

7 - Ridge and Lasso Regression/73 - Regularization in More Complicated Scenarios.mp421MB

7 - Ridge and Lasso Regression/74 - Lasso Regression Basics.mp421.63MB

7 - Ridge and Lasso Regression/75 - Lasso Regression vs Ridge Regression.mp433.86MB

7 - Ridge and Lasso Regression/76 - The Hitters Dataset Preprocessing and Preparation.mp446MB

7 - Ridge and Lasso Regression/77 - Exploratory Data Analysis.mp436.96MB

7 - Ridge and Lasso Regression/78 - Performing Linear Regression.mp452.93MB

7 - Ridge and Lasso Regression/79 - Crossvalidation for Choosing a Tuning Parameter.mp430.49MB

7 - Ridge and Lasso Regression/80 - Performing Ridge Regression with Crossvalidation.mp430.97MB

7 - Ridge and Lasso Regression/81 - Performing Lasso Regression with Crossvalidation.mp430.96MB

7 - Ridge and Lasso Regression/82 - Comparing the Results.mp430.97MB

7 - Ridge and Lasso Regression/83 - Replacing the Missing Values in the DataFrame.mp421.22MB