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

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File Name
Size
0. Websites you may like/[CourseClub.Me].url
122 B
0. Websites you may like/[FreeCourseSite.com].url
127 B
0. Websites you may like/[GigaCourse.Com].url
49 B
1. Introduction to Machine Learning/1. What is Machine Learning.mp4
7.5 MB
1. Introduction to Machine Learning/1. What is Machine Learning.srt
2.1 kB
1. Introduction to Machine Learning/2. Applications of Machine Learning.mp4
6.5 MB
1. Introduction to Machine Learning/2. Applications of Machine Learning.srt
1.9 kB
1. Introduction to Machine Learning/3. Machine learning Methods.mp4
3.7 MB
1. Introduction to Machine Learning/3. Machine learning Methods.srt
437 B
1. Introduction to Machine Learning/4. What is Supervised learning.mp4
6.2 MB
1. Introduction to Machine Learning/4. What is Supervised learning.srt
1.3 kB
1. Introduction to Machine Learning/5. What is Unsupervised learning.mp4
6.0 MB
1. Introduction to Machine Learning/5. What is Unsupervised learning.srt
1.0 kB
1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.mp4
14 MB
1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.srt
4.4 kB
1. Introduction to Machine Learning/7. Course Materials.html
148 B
1. Introduction to Machine Learning/7.1 50_Startups.csv
2.4 kB
1. Introduction to Machine Learning/7.10 Movie_Id_Titles.original
50 kB
1. Introduction to Machine Learning/7.11 MultipleLinearRegression.ipynb
8.5 kB
1. Introduction to Machine Learning/7.12 Recommender Systems with Python.ipynb
122 kB
1. Introduction to Machine Learning/7.13 salaries.csv
657 B
1. Introduction to Machine Learning/7.14 u.data
2.0 MB
1. Introduction to Machine Learning/7.15 user data.csv
11 kB
1. Introduction to Machine Learning/7.2 Decision_tree.ipynb
14 kB
1. Introduction to Machine Learning/7.3 homeprices.csv
77 B
1. Introduction to Machine Learning/7.4 K-means algorithm numpy&pandas clustering.ipynb
102 kB
1. Introduction to Machine Learning/7.5 KNN_Binary_Classification.ipynb
25 kB
1. Introduction to Machine Learning/7.6 linear_regression_houseprice.ipynb
16 kB
1. Introduction to Machine Learning/7.7 logistic_regression_Binary_Classification.ipynb
2.7 kB
1. Introduction to Machine Learning/7.8 mall customers data.csv
4.3 kB
1. Introduction to Machine Learning/7.9 mallCustomerData.txt
3.9 kB
2. Simple Linear Regression/1. Introduction to regression.mp4
9.0 MB
2. Simple Linear Regression/1. Introduction to regression.srt
1.9 kB
2. Simple Linear Regression/2. How Does Linear Regression Work.mp4
7.7 MB
2. Simple Linear Regression/2. How Does Linear Regression Work.srt
1.9 kB
2. Simple Linear Regression/3. Line representation.mp4
5.5 MB
2. Simple Linear Regression/3. Line representation.srt
828 B
2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.mp4
7.6 MB
2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.srt
1.4 kB
2. Simple Linear Regression/5. Implementation in python Distribution of the data.mp4
9.5 MB
2. Simple Linear Regression/5. Implementation in python Distribution of the data.srt
2.2 kB
2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.mp4
13 MB
2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.srt
2.8 kB
3. Multiple Linear Regression/1. Understanding Multiple linear regression.mp4
6.3 MB
3. Multiple Linear Regression/1. Understanding Multiple linear regression.srt
1.4 kB
3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.mp4
13 MB
3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.srt
3.5 kB
3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.mp4
29 MB
3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.srt
5.6 kB
3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.mp4
8.8 MB
3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.srt
1.5 kB
3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.mp4
8.6 MB
3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.srt
1020 B
3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.mp4
18 MB
3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.srt
2.8 kB
3. Multiple Linear Regression/7. Evaluating the performance of the regression model.mp4
6.0 MB
3. Multiple Linear Regression/7. Evaluating the performance of the regression model.srt
1.3 kB
3. Multiple Linear Regression/8. Root Mean Squared Error in Python.mp4
12 MB
3. Multiple Linear Regression/8. Root Mean Squared Error in Python.srt
2.2 kB
4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.mp4
4.7 MB
4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.srt
1.1 kB
4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.mp4
9.7 MB
4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.srt
1.4 kB
4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.mp4
6.1 MB
4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.srt
921 B
4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.mp4
3.5 MB
4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.srt
380 B
4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.mp4
6.1 MB
4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.srt
1.2 kB
4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.mp4
5.1 MB
4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.srt
434 B
4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.mp4
9.3 MB
4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.srt
1.3 kB
4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.mp4
20 MB
4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.srt
2.8 kB
4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.mp4
5.7 MB
4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.srt
348 B
4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.mp4
12 MB
4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.srt
2.0 kB
5. Classification Algorithms Decision Tree/1. Introduction to decision trees.mp4
6.5 MB
5. Classification Algorithms Decision Tree/1. Introduction to decision trees.srt
1.5 kB
5. Classification Algorithms Decision Tree/2. What is Entropy.mp4
5.2 MB
5. Classification Algorithms Decision Tree/2. What is Entropy.srt
1.4 kB
5. Classification Algorithms Decision Tree/3. Exploring the dataset.mp4
6.0 MB
5. Classification Algorithms Decision Tree/3. Exploring the dataset.srt
1.3 kB
5. Classification Algorithms Decision Tree/4. Decision tree structure.mp4
6.4 MB
5. Classification Algorithms Decision Tree/4. Decision tree structure.srt
1.3 kB
5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.mp4
4.6 MB
5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.srt
869 B
5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.mp4
17 MB
5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.srt
3.4 kB
5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.mp4
4.9 MB
5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.srt
879 B
5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.mp4
10 MB
5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.srt
2.7 kB
6. Classification Algorithms Logistic regression/1. Introduction.mp4
6.6 MB
6. Classification Algorithms Logistic regression/1. Introduction.srt
1.4 kB
6. Classification Algorithms Logistic regression/2. Implementation steps.mp4
5.5 MB
6. Classification Algorithms Logistic regression/2. Implementation steps.srt
954 B
6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.mp4
6.8 MB
6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.srt
1.8 kB
6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.mp4
7.2 MB
6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.srt
1.6 kB
6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.mp4
13 MB
6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.srt
1.9 kB
6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.mp4
7.8 MB
6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.srt
1.2 kB
6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.mp4
14 MB
6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.srt
2.5 kB
6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.mp4
11 MB
6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.srt
2.9 kB
7. Clustering/1. Introduction to clustering.mp4
4.3 MB
7. Clustering/1. Introduction to clustering.srt
832 B
7. Clustering/10. Importing the dataset.mp4
13 MB
7. Clustering/10. Importing the dataset.srt
3.3 kB
7. Clustering/11. Visualizing the dataset.mp4
12 MB
7. Clustering/11. Visualizing the dataset.srt
2.9 kB
7. Clustering/12. Defining the classifier.mp4
7.7 MB
7. Clustering/12. Defining the classifier.srt
1.6 kB
7. Clustering/13. 3D Visualization of the clusters.mp4
7.8 MB
7. Clustering/13. 3D Visualization of the clusters.srt
1.6 kB
7. Clustering/14. 3D Visualization of the predicted values.mp4
13 MB
7. Clustering/14. 3D Visualization of the predicted values.srt
2.8 kB
7. Clustering/15. Number of predicted clusters.mp4
9.5 MB
7. Clustering/15. Number of predicted clusters.srt
2.1 kB
7. Clustering/2. Use cases.mp4
4.1 MB
7. Clustering/2. Use cases.srt
1.0 kB
7. Clustering/3. K-Means Clustering Algorithm.mp4
6.6 MB
7. Clustering/3. K-Means Clustering Algorithm.srt
1.5 kB
7. Clustering/4. Elbow method.mp4
7.0 MB
7. Clustering/4. Elbow method.srt
1.7 kB
7. Clustering/5. Steps of the Elbow method.mp4
5.8 MB
7. Clustering/5. Steps of the Elbow method.srt
1.1 kB
7. Clustering/6. Implementation in python.mp4
19 MB
7. Clustering/6. Implementation in python.srt
3.7 kB
7. Clustering/7. Hierarchical clustering.mp4
7.4 MB
7. Clustering/7. Hierarchical clustering.srt
1.3 kB
7. Clustering/8. Density-based clustering.mp4
7.8 MB
7. Clustering/8. Density-based clustering.srt
1.7 kB
7. Clustering/9. Implementation of k-means clustering in python.mp4
3.9 MB
7. Clustering/9. Implementation of k-means clustering in python.srt
836 B
8. Recommender System/1. Introduction.mp4
7.5 MB
8. Recommender System/1. Introduction.srt
1.6 kB
8. Recommender System/10. Data pre-processing.mp4
11 MB
8. Recommender System/10. Data pre-processing.srt
2.2 kB
8. Recommender System/11. Sorting the most-rated movies.mp4
8.9 MB
8. Recommender System/11. Sorting the most-rated movies.srt
879 B
8. Recommender System/12. Grabbing the ratings for two movies.mp4
5.5 MB
8. Recommender System/12. Grabbing the ratings for two movies.srt
1.5 kB
8. Recommender System/13. Correlation between the most-rated movies.mp4
13 MB
8. Recommender System/13. Correlation between the most-rated movies.srt
2.1 kB
8. Recommender System/14. Sorting the data by correlation.mp4
6.1 MB
8. Recommender System/14. Sorting the data by correlation.srt
1.5 kB
8. Recommender System/15. Filtering out movies.mp4
4.8 MB
8. Recommender System/15. Filtering out movies.srt
726 B
8. Recommender System/16. Sorting values.mp4
6.8 MB
8. Recommender System/16. Sorting values.srt
1.1 kB
8. Recommender System/17. Repeating the process for another movie.mp4
13 MB
8. Recommender System/17. Repeating the process for another movie.srt
2.5 kB
8. Recommender System/18. Quiz Time.html
188 B
8. Recommender System/2. Collaborative Filtering in Recommender Systems.mp4
4.2 MB
8. Recommender System/2. Collaborative Filtering in Recommender Systems.srt
674 B
8. Recommender System/3. Content-based Recommender System.mp4
4.9 MB
8. Recommender System/3. Content-based Recommender System.srt
765 B
8. Recommender System/4. Implementation in python Importing libraries & datasets.mp4
10 MB
8. Recommender System/4. Implementation in python Importing libraries & datasets.srt
3.1 kB
8. Recommender System/5. Merging datasets into one dataframe.mp4
4.2 MB
8. Recommender System/5. Merging datasets into one dataframe.srt
622 B
8. Recommender System/6. Sorting by title and rating.mp4
19 MB
8. Recommender System/6. Sorting by title and rating.srt
5.7 kB
8. Recommender System/7. Histogram showing number of ratings.mp4
5.7 MB
8. Recommender System/7. Histogram showing number of ratings.srt
779 B
8. Recommender System/8. Frequency distribution.mp4
6.1 MB
8. Recommender System/8. Frequency distribution.srt
1.3 kB
8. Recommender System/9. Jointplot of the ratings and number of ratings.mp4
7.3 MB
8. Recommender System/9. Jointplot of the ratings and number of ratings.srt
1.3 kB
9. Conclusion/1. Conclusion.mp4
2.8 MB
9. Conclusion/1. Conclusion.srt
414 B