[FreeCourseSite.com] Udemy - Python for Machine Learning The Complete Beginner's Course

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[FreeCourseSite.com] Udemy - Python for Machine Learning The Complete Beginner's Course

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种子哈希：3571aa8bff21e9c64a09fb3709e896e869f06bf2
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收录时间：2024-04-12
最近下载：2024-12-10

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文件列表

  • 3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.mp4 30.3 MB
  • 4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.mp4 20.6 MB
  • 8. Recommender System/6. Sorting by title and rating.mp4 20.3 MB
  • 7. Clustering/6. Implementation in python.mp4 19.9 MB
  • 3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.mp4 18.7 MB
  • 5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.mp4 17.8 MB
  • 1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.mp4 15.0 MB
  • 6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.mp4 14.1 MB
  • 3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.mp4 14.0 MB
  • 8. Recommender System/13. Correlation between the most-rated movies.mp4 13.9 MB
  • 2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.mp4 13.9 MB
  • 6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.mp4 13.8 MB
  • 7. Clustering/14. 3D Visualization of the predicted values.mp4 13.5 MB
  • 7. Clustering/10. Importing the dataset.mp4 13.4 MB
  • 8. Recommender System/17. Repeating the process for another movie.mp4 13.3 MB
  • 4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.mp4 13.1 MB
  • 7. Clustering/11. Visualizing the dataset.mp4 13.0 MB
  • 3. Multiple Linear Regression/8. Root Mean Squared Error in Python.mp4 12.4 MB
  • 8. Recommender System/10. Data pre-processing.mp4 11.3 MB
  • 6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.mp4 11.3 MB
  • 5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.mp4 10.9 MB
  • 8. Recommender System/4. Implementation in python Importing libraries & datasets.mp4 10.8 MB
  • 4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.mp4 10.1 MB
  • 7. Clustering/15. Number of predicted clusters.mp4 9.9 MB
  • 2. Simple Linear Regression/5. Implementation in python Distribution of the data.mp4 9.9 MB
  • 4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.mp4 9.7 MB
  • 2. Simple Linear Regression/1. Introduction to regression.mp4 9.4 MB
  • 8. Recommender System/11. Sorting the most-rated movies.mp4 9.3 MB
  • 3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.mp4 9.3 MB
  • 3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.mp4 9.0 MB
  • 6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.mp4 8.2 MB
  • 7. Clustering/13. 3D Visualization of the clusters.mp4 8.2 MB
  • 7. Clustering/8. Density-based clustering.mp4 8.2 MB
  • 2. Simple Linear Regression/2. How Does Linear Regression Work.mp4 8.1 MB
  • 7. Clustering/12. Defining the classifier.mp4 8.0 MB
  • 2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.mp4 7.9 MB
  • 8. Recommender System/1. Introduction.mp4 7.9 MB
  • 1. Introduction to Machine Learning/1. What is Machine Learning.mp4 7.8 MB
  • 7. Clustering/7. Hierarchical clustering.mp4 7.8 MB
  • 8. Recommender System/9. Jointplot of the ratings and number of ratings.mp4 7.6 MB
  • 6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.mp4 7.5 MB
  • 7. Clustering/4. Elbow method.mp4 7.4 MB
  • 8. Recommender System/16. Sorting values.mp4 7.2 MB
  • 6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.mp4 7.2 MB
  • 7. Clustering/3. K-Means Clustering Algorithm.mp4 6.9 MB
  • 6. Classification Algorithms Logistic regression/1. Introduction.mp4 6.9 MB
  • 1. Introduction to Machine Learning/2. Applications of Machine Learning.mp4 6.8 MB
  • 5. Classification Algorithms Decision Tree/1. Introduction to decision trees.mp4 6.8 MB
  • 5. Classification Algorithms Decision Tree/4. Decision tree structure.mp4 6.7 MB
  • 3. Multiple Linear Regression/1. Understanding Multiple linear regression.mp4 6.6 MB
  • 1. Introduction to Machine Learning/4. What is Supervised learning.mp4 6.5 MB
  • 4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.mp4 6.4 MB
  • 8. Recommender System/14. Sorting the data by correlation.mp4 6.4 MB
  • 8. Recommender System/8. Frequency distribution.mp4 6.3 MB
  • 4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.mp4 6.3 MB
  • 3. Multiple Linear Regression/7. Evaluating the performance of the regression model.mp4 6.3 MB
  • 5. Classification Algorithms Decision Tree/3. Exploring the dataset.mp4 6.2 MB
  • 1. Introduction to Machine Learning/5. What is Unsupervised learning.mp4 6.2 MB
  • 7. Clustering/5. Steps of the Elbow method.mp4 6.1 MB
  • 4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.mp4 6.0 MB
  • 8. Recommender System/7. Histogram showing number of ratings.mp4 5.9 MB
  • 6. Classification Algorithms Logistic regression/2. Implementation steps.mp4 5.8 MB
  • 8. Recommender System/12. Grabbing the ratings for two movies.mp4 5.7 MB
  • 2. Simple Linear Regression/3. Line representation.mp4 5.7 MB
  • 5. Classification Algorithms Decision Tree/2. What is Entropy.mp4 5.5 MB
  • 4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.mp4 5.4 MB
  • 5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.mp4 5.2 MB
  • 8. Recommender System/3. Content-based Recommender System.mp4 5.1 MB
  • 8. Recommender System/15. Filtering out movies.mp4 5.0 MB
  • 4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.mp4 4.9 MB
  • 5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.mp4 4.9 MB
  • 7. Clustering/1. Introduction to clustering.mp4 4.5 MB
  • 8. Recommender System/5. Merging datasets into one dataframe.mp4 4.4 MB
  • 8. Recommender System/2. Collaborative Filtering in Recommender Systems.mp4 4.4 MB
  • 7. Clustering/2. Use cases.mp4 4.2 MB
  • 7. Clustering/9. Implementation of k-means clustering in python.mp4 4.1 MB
  • 1. Introduction to Machine Learning/3. Machine learning Methods.mp4 3.9 MB
  • 4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.mp4 3.7 MB
  • 9. Conclusion/1. Conclusion.mp4 2.9 MB
  • 1. Introduction to Machine Learning/7.14 u.data 2.1 MB
  • 1. Introduction to Machine Learning/7.12 Recommender Systems with Python.ipynb 125.3 kB
  • 1. Introduction to Machine Learning/7.4 K-means algorithm numpy&pandas clustering.ipynb 104.8 kB
  • 1. Introduction to Machine Learning/7.10 Movie_Id_Titles.original 51.0 kB
  • 1. Introduction to Machine Learning/7.5 KNN_Binary_Classification.ipynb 25.8 kB
  • 1. Introduction to Machine Learning/7.6 linear_regression_houseprice.ipynb 16.7 kB
  • 1. Introduction to Machine Learning/7.2 Decision_tree.ipynb 14.7 kB
  • 1. Introduction to Machine Learning/7.15 user data.csv 10.9 kB
  • 1. Introduction to Machine Learning/7.11 MultipleLinearRegression.ipynb 8.7 kB
  • 8. Recommender System/6. Sorting by title and rating.srt 5.8 kB
  • 3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.srt 5.8 kB
  • 1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.srt 4.6 kB
  • 1. Introduction to Machine Learning/7.8 mall customers data.csv 4.4 kB
  • 1. Introduction to Machine Learning/7.9 mallCustomerData.txt 4.0 kB
  • 7. Clustering/6. Implementation in python.srt 3.7 kB
  • 3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.srt 3.6 kB
  • 5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.srt 3.5 kB
  • 7. Clustering/10. Importing the dataset.srt 3.3 kB
  • 8. Recommender System/4. Implementation in python Importing libraries & datasets.srt 3.2 kB
  • 7. Clustering/11. Visualizing the dataset.srt 2.9 kB
  • 6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.srt 2.9 kB
  • 4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.srt 2.9 kB
  • 3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.srt 2.9 kB
  • 2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.srt 2.9 kB
  • 7. Clustering/14. 3D Visualization of the predicted values.srt 2.8 kB
  • 1. Introduction to Machine Learning/7.7 logistic_regression_Binary_Classification.ipynb 2.8 kB
  • 5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.srt 2.7 kB
  • 8. Recommender System/17. Repeating the process for another movie.srt 2.6 kB
  • 6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.srt 2.6 kB
  • 1. Introduction to Machine Learning/7.1 50_Startups.csv 2.4 kB
  • 3. Multiple Linear Regression/8. Root Mean Squared Error in Python.srt 2.3 kB
  • 8. Recommender System/10. Data pre-processing.srt 2.2 kB
  • 2. Simple Linear Regression/5. Implementation in python Distribution of the data.srt 2.2 kB
  • 7. Clustering/15. Number of predicted clusters.srt 2.1 kB
  • 1. Introduction to Machine Learning/1. What is Machine Learning.srt 2.1 kB
  • 8. Recommender System/13. Correlation between the most-rated movies.srt 2.1 kB
  • 4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.srt 2.0 kB
  • 1. Introduction to Machine Learning/2. Applications of Machine Learning.srt 2.0 kB
  • 6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.srt 1.9 kB
  • 2. Simple Linear Regression/1. Introduction to regression.srt 1.9 kB
  • 2. Simple Linear Regression/2. How Does Linear Regression Work.srt 1.9 kB
  • 6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.srt 1.9 kB
  • 7. Clustering/4. Elbow method.srt 1.8 kB
  • 7. Clustering/8. Density-based clustering.srt 1.8 kB
  • 7. Clustering/12. Defining the classifier.srt 1.7 kB
  • 6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.srt 1.6 kB
  • 7. Clustering/13. 3D Visualization of the clusters.srt 1.6 kB
  • 8. Recommender System/1. Introduction.srt 1.6 kB
  • 7. Clustering/3. K-Means Clustering Algorithm.srt 1.6 kB
  • 3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.srt 1.6 kB
  • 5. Classification Algorithms Decision Tree/1. Introduction to decision trees.srt 1.5 kB
  • 8. Recommender System/12. Grabbing the ratings for two movies.srt 1.5 kB
  • 8. Recommender System/14. Sorting the data by correlation.srt 1.5 kB
  • 2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.srt 1.5 kB
  • 3. Multiple Linear Regression/1. Understanding Multiple linear regression.srt 1.5 kB
  • 5. Classification Algorithms Decision Tree/2. What is Entropy.srt 1.5 kB
  • 6. Classification Algorithms Logistic regression/1. Introduction.srt 1.5 kB
  • 4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.srt 1.4 kB
  • 8. Recommender System/9. Jointplot of the ratings and number of ratings.srt 1.4 kB
  • 5. Classification Algorithms Decision Tree/3. Exploring the dataset.srt 1.4 kB
  • 5. Classification Algorithms Decision Tree/4. Decision tree structure.srt 1.4 kB
  • 3. Multiple Linear Regression/7. Evaluating the performance of the regression model.srt 1.3 kB
  • 1. Introduction to Machine Learning/4. What is Supervised learning.srt 1.3 kB
  • 4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.srt 1.3 kB
  • 7. Clustering/7. Hierarchical clustering.srt 1.3 kB
  • 8. Recommender System/8. Frequency distribution.srt 1.3 kB
  • 4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.srt 1.2 kB
  • 6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.srt 1.2 kB
  • 4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.srt 1.2 kB
  • 8. Recommender System/16. Sorting values.srt 1.1 kB
  • 7. Clustering/5. Steps of the Elbow method.srt 1.1 kB
  • 1. Introduction to Machine Learning/5. What is Unsupervised learning.srt 1.0 kB
  • 7. Clustering/2. Use cases.srt 1.0 kB
  • 3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.srt 1.0 kB
  • 6. Classification Algorithms Logistic regression/2. Implementation steps.srt 954 Bytes
  • 4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.srt 921 Bytes
  • 5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.srt 879 Bytes
  • 8. Recommender System/11. Sorting the most-rated movies.srt 879 Bytes
  • 5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.srt 869 Bytes
  • 7. Clustering/9. Implementation of k-means clustering in python.srt 836 Bytes
  • 7. Clustering/1. Introduction to clustering.srt 832 Bytes
  • 2. Simple Linear Regression/3. Line representation.srt 828 Bytes
  • 8. Recommender System/7. Histogram showing number of ratings.srt 779 Bytes
  • 8. Recommender System/3. Content-based Recommender System.srt 765 Bytes
  • 8. Recommender System/15. Filtering out movies.srt 726 Bytes
  • 8. Recommender System/2. Collaborative Filtering in Recommender Systems.srt 674 Bytes
  • 1. Introduction to Machine Learning/7.13 salaries.csv 657 Bytes
  • 8. Recommender System/5. Merging datasets into one dataframe.srt 622 Bytes
  • 1. Introduction to Machine Learning/3. Machine learning Methods.srt 437 Bytes
  • 4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.srt 434 Bytes
  • 9. Conclusion/1. Conclusion.srt 414 Bytes
  • 4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.srt 380 Bytes
  • 4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.srt 348 Bytes
  • 8. Recommender System/18. Quiz Time.html 188 Bytes
  • 1. Introduction to Machine Learning/7. Course Materials.html 148 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 1. Introduction to Machine Learning/7.3 homeprices.csv 77 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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