Udemy - Python for Machine Learning: The Complete Beginner's Cour...

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• 1. What is Machine Learning.mp4 (7.5 MB)
• 1. What is Machine Learning.srt (2.1 KB)
• 2. Applications of Machine Learning.mp4 (6.5 MB)
• 2. Applications of Machine Learning.srt (1.9 KB)
• 3. Machine learning Methods.mp4 (3.7 MB)
• 3. Machine learning Methods.srt (0.4 KB)
• 4. What is Supervised learning.mp4 (6.2 MB)
• 4. What is Supervised learning.srt (1.3 KB)
• 5. What is Unsupervised learning.mp4 (6.0 MB)
• 5. What is Unsupervised learning.srt (1.0 KB)
• 6. Supervised learning vs Unsupervised learning.mp4 (14.3 MB)
• 6. Supervised learning vs Unsupervised learning.srt (4.4 KB)
• 7. Course Materials.html (0.1 KB)
• 7.1 50_Startups.csv (2.4 KB)
• 7.10 Movie_Id_Titles.original (49.8 KB)
• 7.11 MultipleLinearRegression.ipynb (8.5 KB)
• 7.12 Recommender Systems with Python.ipynb (122.4 KB)
• 7.13 salaries.csv (0.6 KB)
• 7.14 u.data (2.0 MB)
• 7.15 user data.csv (10.7 KB)
• 7.2 Decision_tree.ipynb (14.3 KB)
• 7.3 homeprices.csv (0.1 KB)
• 7.4 K-means algorithm numpy&pandas clustering.ipynb (102.3 KB)
• 7.5 KNN_Binary_Classification.ipynb (25.2 KB)
• 7.6 linear_regression_houseprice.ipynb (16.3 KB)
• 7.7 logistic_regression_Binary_Classification.ipynb (2.7 KB)
• 7.8 mall customers data.csv (4.3 KB)
• 7.9 mallCustomerData.txt (3.9 KB)

• 1. Introduction to regression.mp4 (9.0 MB)
• 1. Introduction to regression.srt (1.9 KB)
• 2. How Does Linear Regression Work.mp4 (7.7 MB)
• 2. How Does Linear Regression Work.srt (1.9 KB)
• 3. Line representation.mp4 (5.5 MB)
• 3. Line representation.srt (0.8 KB)
• 4. Implementation in python Importing libraries & datasets.mp4 (7.6 MB)
• 4. Implementation in python Importing libraries & datasets.srt (1.4 KB)
• 5. Implementation in python Distribution of the data.mp4 (9.5 MB)
• 5. Implementation in python Distribution of the data.srt (2.2 KB)
• 6. Implementation in python Creating a linear regression object.mp4 (13.2 MB)
• 6. Implementation in python Creating a linear regression object.srt (2.8 KB)

• 1. Understanding Multiple linear regression.mp4 (6.3 MB)
• 1. Understanding Multiple linear regression.srt (1.4 KB)
• 2. Implementation in python Exploring the dataset.mp4 (13.3 MB)
• 2. Implementation in python Exploring the dataset.srt (3.5 KB)
• 3. Implementation in python Encoding Categorical Data.mp4 (28.9 MB)
• 3. Implementation in python Encoding Categorical Data.srt (5.6 KB)
• 4. Implementation in python Splitting data into Train and Test Sets.mp4 (8.8 MB)
• 4. Implementation in python Splitting data into Train and Test Sets.srt (1.5 KB)
• 5. Implementation in python Training the model on the Training set.mp4 (8.6 MB)
• 5. Implementation in python Training the model on the Training set.srt (1.0 KB)
• 6. Implementation in python Predicting the Test Set results.mp4 (17.8 MB)
• 6. Implementation in python Predicting the Test Set results.srt (2.8 KB)
• 7. Evaluating the performance of the regression model.mp4 (6.0 MB)
• 7. Evaluating the performance of the regression model.srt (1.3 KB)
• 8. Root Mean Squared Error in Python.mp4 (11.8 MB)
• 8. Root Mean Squared Error in Python.srt (2.2 KB)

• 1. Introduction to classification.mp4 (4.7 MB)
• 1. Introduction to classification.srt (1.1 KB)
• 10. Implementation in python Results prediction & Confusion matrix.mp4 (9.7 MB)
• 10. Implementation in python Results prediction & Confusion matrix.srt (1.4 KB)
• 2. K-Nearest Neighbors algorithm.mp4 (6.1 MB)
• 2. K-Nearest Neighbors algorithm.srt (0.9 KB)
• 3. Example of KNN.mp4 (3.5 MB)
• 3. Example of KNN.srt (0.4 KB)
• 4. K-Nearest Neighbours (KNN) using python.mp4 (6.1 MB)
• 4. K-Nearest Neighbours (KNN) using python.srt (1.2 KB)
• 5. Implementation in python Importing required libraries.mp4 (5.1 MB)
• 5. Implementation in python Importing required libraries.srt (0.4 KB)
• 6. Implementation in python Importing the dataset.mp4 (9.3 MB)
• 6. Implementation in python Importing the dataset.srt (1.3 KB)
• 7. Implementation in python Splitting data into Train and Test Sets.mp4 (19.7 MB)
• 7. Implementation in python Splitting data into Train and Test Sets.srt (2.8 KB)
• 8. Implementation in python Feature Scaling.mp4 (5.7 MB)
• 8. Implementation in python Feature Scaling.srt (0.3 KB)
• 9. Implementation in python Importing the KNN classifier.mp4 (12.5 MB)
• 9. Implementation in python Importing the KNN classifier.srt (2.0 KB)

• 1. Introduction to decision trees.mp4 (6.5 MB)
• 1. Introduction to decision trees.srt (1.5 KB)
• 2. What is Entropy.mp4 (5.2 MB)
• 2. What is Entropy.srt (1.4 KB)
• 3. Exploring the dataset.mp4 (6.0 MB)
• 3. Exploring the dataset.srt (1.3 KB)
• 4. Decision tree structure.mp4 (6.4 MB)
• 4. Decision tree structure.srt (1.3 KB)
• 5. Implementation in python Importing libraries & datasets.mp4 (4.6 MB)
• 5. Implementation in python Importing libraries & datasets.srt (0.8 KB)
• 6. Implementation in python Encoding Categorical Data.mp4 (17.0 MB)
• 6. Implementation in python Encoding Categorical Data.srt (3.4 KB)
• 7. Implementation in python Splitting data into Train and Test Sets.mp4 (4.9 MB)
• 7. Implementation in python Splitting data into Train and Test Sets.srt (0.9 KB)
• 8. Implementation in python Results prediction & Accuracy.mp4 (10.4 MB)
• 8. Implementation in python Results prediction & Accuracy.srt (2.7 KB)

• 1. Introduction.mp4 (6.6 MB)
• 1. Introduction.srt (1.4 KB)
• 2. Implementation steps.mp4 (5.5 MB)
• 2. Implementation steps.srt (0.9 KB)
• 3. Implementation in python Importing libraries & datasets.mp4 (6.8 MB)
• 3. Implementation in python Importing libraries & datasets.srt (1.8 KB)
• 4. Implementation in python Splitting data into Train and Test Sets.mp4 (7.2 MB)
• 4. Implementation in python Splitting data into Train and Test Sets.srt (1.6 KB)
• 5. Implementation in python Pre-processing.mp4 (13.2 MB)
• 5. Implementation in python Pre-processing.srt (1.9 KB)
• 6. Implementation in python Training the model.mp4 (7.8 MB)
• 6. Implementation in python Training the model.srt (1.2 KB)
• 7. Implementation in python Results prediction & Confusion matrix.mp4 (13.5 MB)
• 7. Implementation in python Results prediction & Confusion matrix.srt (2.5 KB)
• 8. Logistic Regression vs Linear Regression.mp4 (10.8 MB)
• 8. Logistic Regression vs Linear Regression.srt (2.9 KB)

• 1. Introduction to clustering.mp4 (4.3 MB)
• 1. Introduction to clustering.srt (0.8 KB)
• 10. Importing the dataset.mp4 (12.8 MB)
• 10. Importing the dataset.srt (3.3 KB)
• 11. Visualizing the dataset.mp4 (12.4 MB)
• 11. Visualizing the dataset.srt (2.9 KB)
• 12. Defining the classifier.mp4 (7.7 MB)
• 12. Defining the classifier.srt (1.6 KB)
• 13. 3D Visualization of the clusters.mp4 (7.8 MB)
• 13. 3D Visualization of the clusters.srt (1.6 KB)
• 14. 3D Visualization of the predicted values.mp4 (12.8 MB)
• 14. 3D Visualization of the predicted values.srt (2.8 KB)
• 15. Number of predicted clusters.mp4 (9.5 MB)
• 15. Number of predicted clusters.srt (2.1 KB)
• 2. Use cases.mp4 (4.1 MB)
• 2. Use cases.srt (1.0 KB)
• 3. K-Means Clustering Algorithm.mp4 (6.6 MB)
• 3. K-Means Clustering Algorithm.srt (1.5 KB)
• 4. Elbow method.mp4 (7.0 MB)
• 4. Elbow method.srt (1.7 KB)
• 5. Steps of the Elbow method.mp4 (5.8 MB)
• 5. Steps of the Elbow method.srt (1.1 KB)
• 6. Implementation in python.mp4 (19.0 MB)
• 6. Implementation in python.srt (3.7 KB)
• 7. Hierarchical clustering.mp4 (7.4 MB)
• 7. Hierarchical clustering.srt (1.3 KB)
• 8. Density-based clustering.mp4 (7.8 MB)
• 8. Density-based clustering.srt (1.7 KB)
• 9. Implementation of k-means clustering in python.mp4 (3.9 MB)
• 9. Implementation of k-means clustering in python.srt (0.8 KB)

• 1. Introduction.mp4 (7.5 MB)
• 1. Introduction.srt (1.6 KB)
• 10. Data pre-processing.mp4 (10.8 MB)
• 10. Data pre-processing.srt (2.2 KB)
• 11. Sorting the most-rated movies.mp4 (8.9 MB)
• 11. Sorting the most-rated movies.srt (0.9 KB)
• 12. Grabbing the ratings for two movies.mp4 (5.5 MB)
• 12. Grabbing the ratings for two movies.srt (1.5 KB)
• 13. Correlation between the most-rated movies.mp4 (13.3 MB)
• 13. Correlation between the most-rated movies.srt (2.1 KB)
• 14. Sorting the data by correlation.mp4 (6.1 MB)
• 14. Sorting the data by correlation.srt (1.5 KB)
• 15. Filtering out movies.mp4 (4.8 MB)
• 15. Filtering out movies.srt (0.7 KB)
• 16. Sorting values.mp4 (6.8 MB)
• 16. Sorting values.srt (1.1 KB)
• 17. Repeating the process for another movie.mp4 (12.7 MB)
• 17. Repeating the process for another movie.srt (2.5 KB)
• 18. Quiz Time.html (0.2 KB)
• 2. Collaborative Filtering in Recommender Systems.mp4 (4.2 MB)
• 2. Collaborative Filtering in Recommender Systems.srt (0.7 KB)
• 3. Content-based Recommender System.mp4 (4.9 MB)
• 3. Content-based Recommender System.srt (0.7 KB)
• 4. Implementation in python Importing libraries & datasets.mp4 (10.3 MB)
• 4. Implementation in python Importing libraries & datasets.srt (3.1 KB)
• 5. Merging datasets into one dataframe.mp4 (4.2 MB)
• 5. Merging datasets into one dataframe.srt (0.6 KB)
• 6. Sorting by title and rating.mp4 (19.3 MB)
• 6. Sorting by title and rating.srt (5.7 KB)
• 7. Histogram showing number of ratings.mp4 (5.7 MB)
• 7. Histogram showing number of ratings.srt (0.8 KB)
• 8. Frequency distribution.mp4 (6.1 MB)
• 8. Frequency distribution.srt (1.3 KB)
• 9. Jointplot of the ratings and number of ratings.mp4 (7.3 MB)
• 9. Jointplot of the ratings and number of ratings.srt (1.3 KB)

• 1. Conclusion.mp4 (2.8 MB)
• 1. Conclusion.srt (0.4 KB)