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1. Conclusion.mp4
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MP4
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2.8 MB
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1. Conclusion.srt
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409.6 B
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1. Introduction to classification.mp4
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MP4
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4.7 MB
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1. Introduction to classification.srt
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SRT
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1.1 KB
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1. Introduction to clustering.mp4
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MP4
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4.3 MB
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1. Introduction to clustering.srt
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SRT
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819.2 B
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1. Introduction to decision trees.mp4
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MP4
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6.5 MB
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1. Introduction to decision trees.srt
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SRT
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1.5 KB
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1. Introduction to regression.mp4
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MP4
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9 MB
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1. Introduction to regression.srt
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SRT
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1.9 KB
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1. Introduction.mp4
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MP4
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7.5 MB
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1. Introduction.srt
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1.6 KB
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1. Understanding Multiple linear regression.mp4
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MP4
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6.3 MB
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1. Understanding Multiple linear regression.srt
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SRT
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1.4 KB
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1. What is Machine Learning.mp4
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MP4
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7.5 MB
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1. What is Machine Learning.srt
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SRT
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2.1 KB
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10. Data pre-processing.mp4
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MP4
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10.8 MB
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10. Data pre-processing.srt
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SRT
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2.2 KB
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10. Implementation in python Results prediction & Confusion matrix.mp4
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MP4
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9.7 MB
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10. Implementation in python Results prediction & Confusion matrix.srt
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SRT
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1.4 KB
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10. Importing the dataset.mp4
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MP4
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12.8 MB
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10. Importing the dataset.srt
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SRT
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3.3 KB
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11. Sorting the most-rated movies.mp4
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MP4
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8.9 MB
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11. Sorting the most-rated movies.srt
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SRT
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921.6 B
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11. Visualizing the dataset.mp4
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MP4
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12.4 MB
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11. Visualizing the dataset.srt
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SRT
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2.9 KB
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12. Defining the classifier.mp4
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MP4
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7.7 MB
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12. Defining the classifier.srt
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SRT
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1.6 KB
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12. Grabbing the ratings for two movies.mp4
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MP4
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5.5 MB
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12. Grabbing the ratings for two movies.srt
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SRT
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1.5 KB
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13. 3D Visualization of the clusters.mp4
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MP4
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7.8 MB
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13. 3D Visualization of the clusters.srt
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SRT
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1.6 KB
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13. Correlation between the most-rated movies.mp4
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MP4
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13.3 MB
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13. Correlation between the most-rated movies.srt
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SRT
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2.1 KB
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14. 3D Visualization of the predicted values.mp4
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MP4
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12.8 MB
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14. 3D Visualization of the predicted values.srt
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SRT
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2.8 KB
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14. Sorting the data by correlation.mp4
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MP4
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6.1 MB
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14. Sorting the data by correlation.srt
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SRT
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1.5 KB
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15. Filtering out movies.mp4
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MP4
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4.8 MB
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15. Filtering out movies.srt
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716.8 B
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15. Number of predicted clusters.mp4
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MP4
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9.5 MB
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15. Number of predicted clusters.srt
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SRT
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2.1 KB
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16. Sorting values.mp4
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MP4
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6.8 MB
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16. Sorting values.srt
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SRT
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1.1 KB
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17. Repeating the process for another movie.mp4
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MP4
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12.7 MB
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17. Repeating the process for another movie.srt
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SRT
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2.5 KB
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18. Quiz Time.html
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HTML
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204.8 B
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2. Applications of Machine Learning.mp4
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MP4
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6.5 MB
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2. Applications of Machine Learning.srt
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SRT
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1.9 KB
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2. Collaborative Filtering in Recommender Systems.mp4
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MP4
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4.2 MB
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2. Collaborative Filtering in Recommender Systems.srt
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SRT
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716.8 B
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2. How Does Linear Regression Work.mp4
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MP4
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7.7 MB
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2. How Does Linear Regression Work.srt
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SRT
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1.9 KB
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2. Implementation in python Exploring the dataset.mp4
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MP4
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13.3 MB
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2. Implementation in python Exploring the dataset.srt
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SRT
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3.5 KB
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2. Implementation steps.mp4
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MP4
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5.5 MB
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2. Implementation steps.srt
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SRT
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921.6 B
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2. K-Nearest Neighbors algorithm.mp4
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MP4
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6.1 MB
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2. K-Nearest Neighbors algorithm.srt
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SRT
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921.6 B
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2. Use cases.mp4
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MP4
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4.1 MB
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2. Use cases.srt
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SRT
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1 KB
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2. What is Entropy.mp4
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MP4
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5.2 MB
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2. What is Entropy.srt
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SRT
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1.4 KB
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3. Content-based Recommender System.mp4
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MP4
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4.9 MB
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3. Content-based Recommender System.srt
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SRT
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716.8 B
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3. Example of KNN.mp4
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MP4
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3.5 MB
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3. Example of KNN.srt
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SRT
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409.6 B
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3. Exploring the dataset.mp4
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MP4
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6 MB
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3. Exploring the dataset.srt
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SRT
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1.3 KB
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3. Implementation in python Encoding Categorical Data.mp4
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MP4
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28.9 MB
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3. Implementation in python Encoding Categorical Data.srt
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SRT
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5.6 KB
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3. Implementation in python Importing libraries & datasets.mp4
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MP4
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6.8 MB
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3. Implementation in python Importing libraries & datasets.srt
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SRT
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1.8 KB
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3. K-Means Clustering Algorithm.mp4
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MP4
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6.6 MB
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3. K-Means Clustering Algorithm.srt
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SRT
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1.5 KB
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3. Line representation.mp4
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MP4
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5.5 MB
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3. Line representation.srt
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SRT
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819.2 B
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3. Machine learning Methods.mp4
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MP4
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3.7 MB
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3. Machine learning Methods.srt
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SRT
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409.6 B
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4. Decision tree structure.mp4
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MP4
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6.4 MB
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4. Decision tree structure.srt
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SRT
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1.3 KB
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4. Elbow method.mp4
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MP4
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7 MB
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4. Elbow method.srt
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SRT
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1.7 KB
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4. Implementation in python Importing libraries & datasets.mp4
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MP4
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10.3 MB
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4. Implementation in python Importing libraries & datasets.srt
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SRT
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3.1 KB
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4. Implementation in python Splitting data into Train and Test Sets.mp4
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MP4
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7.2 MB
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4. Implementation in python Splitting data into Train and Test Sets.srt
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SRT
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1.6 KB
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4. K-Nearest Neighbours (KNN) using python.mp4
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MP4
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6.1 MB
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4. K-Nearest Neighbours (KNN) using python.srt
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SRT
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1.2 KB
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4. What is Supervised learning.mp4
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MP4
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6.2 MB
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4. What is Supervised learning.srt
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SRT
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1.3 KB
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5. Implementation in python Distribution of the data.mp4
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MP4
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9.5 MB
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5. Implementation in python Distribution of the data.srt
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SRT
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2.2 KB
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5. Implementation in python Importing libraries & datasets.mp4
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MP4
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4.6 MB
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5. Implementation in python Importing libraries & datasets.srt
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SRT
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819.2 B
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5. Implementation in python Importing required libraries.mp4
|
MP4
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5.1 MB
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5. Implementation in python Importing required libraries.srt
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SRT
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409.6 B
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5. Implementation in python Pre-processing.mp4
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MP4
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13.2 MB
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5. Implementation in python Pre-processing.srt
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SRT
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1.9 KB
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5. Implementation in python Training the model on the Training set.mp4
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MP4
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8.6 MB
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5. Implementation in python Training the model on the Training set.srt
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SRT
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1 KB
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5. Merging datasets into one dataframe.mp4
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MP4
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4.2 MB
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5. Merging datasets into one dataframe.srt
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SRT
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614.4 B
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5. Steps of the Elbow method.mp4
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MP4
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5.8 MB
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5. Steps of the Elbow method.srt
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SRT
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1.1 KB
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5. What is Unsupervised learning.mp4
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MP4
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6 MB
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5. What is Unsupervised learning.srt
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SRT
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1 KB
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6. Implementation in python Creating a linear regression object.mp4
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MP4
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13.2 MB
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6. Implementation in python Creating a linear regression object.srt
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SRT
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2.8 KB
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6. Implementation in python Encoding Categorical Data.mp4
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MP4
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17 MB
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6. Implementation in python Encoding Categorical Data.srt
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SRT
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3.4 KB
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6. Implementation in python Importing the dataset.mp4
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MP4
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9.3 MB
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6. Implementation in python Importing the dataset.srt
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SRT
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1.3 KB
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6. Implementation in python Predicting the Test Set results.mp4
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MP4
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17.8 MB
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6. Implementation in python Predicting the Test Set results.srt
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SRT
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2.8 KB
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6. Implementation in python Training the model.mp4
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MP4
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7.8 MB
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6. Implementation in python Training the model.srt
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SRT
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1.2 KB
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6. Implementation in python.mp4
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MP4
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19 MB
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6. Implementation in python.srt
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SRT
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3.7 KB
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6. Sorting by title and rating.mp4
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MP4
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19.3 MB
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6. Sorting by title and rating.srt
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SRT
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5.7 KB
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6. Supervised learning vs Unsupervised learning.mp4
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MP4
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14.3 MB
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6. Supervised learning vs Unsupervised learning.srt
|
SRT
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4.4 KB
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7. Course Materials.html
|
HTML
|
102.4 B
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7. Evaluating the performance of the regression model.mp4
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MP4
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6 MB
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7. Evaluating the performance of the regression model.srt
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SRT
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1.3 KB
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7. Hierarchical clustering.mp4
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MP4
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7.4 MB
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7. Hierarchical clustering.srt
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SRT
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1.3 KB
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7. Histogram showing number of ratings.mp4
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MP4
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5.7 MB
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7. Histogram showing number of ratings.srt
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SRT
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819.2 B
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7. Implementation in python Results prediction & Confusion matrix.mp4
|
MP4
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13.5 MB
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7. Implementation in python Results prediction & Confusion matrix.srt
|
SRT
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2.5 KB
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7. Implementation in python Splitting data into Train and Test Sets.mp4
|
MP4
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4.9 MB
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7. Implementation in python Splitting data into Train and Test Sets.srt
|
SRT
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921.6 B
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7.1 50_Startups.csv
|
CSV
|
2.4 KB
|
|
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7.10 Movie_Id_Titles.original
|
ORIGINAL
|
49.8 KB
|
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7.11 MultipleLinearRegression.ipynb
|
IPYNB
|
8.5 KB
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7.12 Recommender Systems with Python.ipynb
|
IPYNB
|
122.4 KB
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7.13 salaries.csv
|
CSV
|
614.4 B
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7.14 u.data
|
DATA
|
2 MB
|
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7.15 user data.csv
|
CSV
|
10.7 KB
|
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7.2 Decision_tree.ipynb
|
IPYNB
|
14.3 KB
|
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7.3 homeprices.csv
|
CSV
|
102.4 B
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7.4 K-means algorithm numpy&pandas clustering.ipynb
|
IPYNB
|
102.3 KB
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7.5 KNN_Binary_Classification.ipynb
|
IPYNB
|
25.2 KB
|
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7.6 linear_regression_houseprice.ipynb
|
IPYNB
|
16.3 KB
|
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7.7 logistic_regression_Binary_Classification.ipynb
|
IPYNB
|
2.7 KB
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7.8 mall customers data.csv
|
CSV
|
4.3 KB
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7.9 mallCustomerData.txt
|
TXT
|
3.9 KB
|
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8. Density-based clustering.mp4
|
MP4
|
7.8 MB
|
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8. Density-based clustering.srt
|
SRT
|
1.7 KB
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8. Frequency distribution.mp4
|
MP4
|
6.1 MB
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8. Frequency distribution.srt
|
SRT
|
1.3 KB
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8. Implementation in python Feature Scaling.mp4
|
MP4
|
5.7 MB
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8. Implementation in python Feature Scaling.srt
|
SRT
|
307.2 B
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8. Implementation in python Results prediction & Accuracy.mp4
|
MP4
|
10.4 MB
|
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8. Implementation in python Results prediction & Accuracy.srt
|
SRT
|
2.7 KB
|
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8. Logistic Regression vs Linear Regression.mp4
|
MP4
|
10.8 MB
|
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8. Logistic Regression vs Linear Regression.srt
|
SRT
|
2.9 KB
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8. Root Mean Squared Error in Python.mp4
|
MP4
|
11.8 MB
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8. Root Mean Squared Error in Python.srt
|
SRT
|
2.2 KB
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9. Implementation in python Importing the KNN classifier.mp4
|
MP4
|
12.5 MB
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9. Implementation in python Importing the KNN classifier.srt
|
SRT
|
2 KB
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9. Implementation of k-means clustering in python.mp4
|
MP4
|
3.9 MB
|
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9. Implementation of k-means clustering in python.srt
|
SRT
|
819.2 B
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9. Jointplot of the ratings and number of ratings.mp4
|
MP4
|
7.3 MB
|
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9. Jointplot of the ratings and number of ratings.srt
|
SRT
|
1.3 KB
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[CourseClub.Me].url
|
URL
|
102.4 B
|
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[FreeCourseSite.com].url
|
URL
|
102.4 B
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[GigaCourse.Com].url
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URL
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0 B
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![Udemy - Python for Machine Learning: The Complete Beginner's Course [FCS]](/static/img/no-torrent-image.png "Udemy - Python for Machine Learning: The Complete Beginner's Course [FCS]")
