种子简介

种子名称： [GigaCourse.Com] Udemy - Python for Machine Learning The Complete Beginner's Course
文件类型： 视频
文件数目： 80个文件
文件大小： 684.84 MB
收录时间： 2023-4-13 00:26
已经下载： 3次
资源热度： 158
最近下载： 2024-11-23 06:47

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:53d8351e5f14e26cf79e4c81c021dc0c662b9dd3&dn=[GigaCourse.Com] Udemy - Python for Machine Learning The Complete Beginner's Course 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[GigaCourse.Com] Udemy - Python for Machine Learning The Complete Beginner's Course.torrent

1. Introduction to Machine Learning/1. What is Machine Learning.mp47.48MB

1. Introduction to Machine Learning/2. Applications of Machine Learning.mp46.51MB

1. Introduction to Machine Learning/3. Machine learning Methods.mp43.7MB

1. Introduction to Machine Learning/4. What is Supervised learning.mp46.23MB

1. Introduction to Machine Learning/5. What is Unsupervised learning.mp45.95MB

1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.mp414.33MB

1. Introduction to Machine Learning/7.14 u.data1.98MB

2. Simple Linear Regression/1. Introduction to regression.mp48.97MB

2. Simple Linear Regression/2. How Does Linear Regression Work.mp47.68MB

2. Simple Linear Regression/3. Line representation.mp45.45MB

2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.mp47.55MB

2. Simple Linear Regression/5. Implementation in python Distribution of the data.mp49.46MB

2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.mp413.22MB

3. Multiple Linear Regression/1. Understanding Multiple linear regression.mp46.32MB

3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.mp413.31MB

3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.mp428.92MB

3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.mp48.83MB

3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.mp48.62MB

3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.mp417.83MB

3. Multiple Linear Regression/7. Evaluating the performance of the regression model.mp46.01MB

3. Multiple Linear Regression/8. Root Mean Squared Error in Python.mp411.83MB

4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.mp44.67MB

4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.mp49.67MB

4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.mp46.05MB

4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.mp43.48MB

4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.mp46.14MB

4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.mp45.11MB

4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.mp49.29MB

4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.mp419.69MB

4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.mp45.73MB

4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.mp412.51MB

5. Classification Algorithms Decision Tree/1. Introduction to decision trees.mp46.49MB

5. Classification Algorithms Decision Tree/2. What is Entropy.mp45.23MB

5. Classification Algorithms Decision Tree/3. Exploring the dataset.mp45.96MB

5. Classification Algorithms Decision Tree/4. Decision tree structure.mp46.39MB

5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.mp44.65MB

5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.mp416.98MB

5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.mp44.92MB

5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.mp410.44MB

6. Classification Algorithms Logistic regression/1. Introduction.mp46.59MB

6. Classification Algorithms Logistic regression/2. Implementation steps.mp45.49MB

6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.mp46.82MB

6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.mp47.18MB

6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.mp413.17MB

6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.mp47.83MB

6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.mp413.46MB

6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.mp410.76MB

7. Clustering/1. Introduction to clustering.mp44.26MB

7. Clustering/10. Importing the dataset.mp412.78MB

7. Clustering/11. Visualizing the dataset.mp412.43MB

7. Clustering/12. Defining the classifier.mp47.66MB

7. Clustering/13. 3D Visualization of the clusters.mp47.82MB

7. Clustering/14. 3D Visualization of the predicted values.mp412.84MB

7. Clustering/15. Number of predicted clusters.mp49.49MB

7. Clustering/2. Use cases.mp44.05MB

7. Clustering/3. K-Means Clustering Algorithm.mp46.62MB

7. Clustering/4. Elbow method.mp47.02MB

7. Clustering/5. Steps of the Elbow method.mp45.84MB

7. Clustering/6. Implementation in python.mp419MB

7. Clustering/7. Hierarchical clustering.mp47.42MB

7. Clustering/8. Density-based clustering.mp47.79MB

7. Clustering/9. Implementation of k-means clustering in python.mp43.93MB

8. Recommender System/1. Introduction.mp47.54MB

8. Recommender System/10. Data pre-processing.mp410.76MB

8. Recommender System/11. Sorting the most-rated movies.mp48.88MB

8. Recommender System/12. Grabbing the ratings for two movies.mp45.47MB

8. Recommender System/13. Correlation between the most-rated movies.mp413.29MB

8. Recommender System/14. Sorting the data by correlation.mp46.14MB

8. Recommender System/15. Filtering out movies.mp44.79MB

8. Recommender System/16. Sorting values.mp46.84MB

8. Recommender System/17. Repeating the process for another movie.mp412.66MB

8. Recommender System/2. Collaborative Filtering in Recommender Systems.mp44.16MB

8. Recommender System/3. Content-based Recommender System.mp44.88MB

8. Recommender System/4. Implementation in python Importing libraries & datasets.mp410.26MB

8. Recommender System/5. Merging datasets into one dataframe.mp44.19MB

8. Recommender System/6. Sorting by title and rating.mp419.33MB

8. Recommender System/7. Histogram showing number of ratings.mp45.67MB

8. Recommender System/8. Frequency distribution.mp46.05MB

8. Recommender System/9. Jointplot of the ratings and number of ratings.mp47.28MB

9. Conclusion/1. Conclusion.mp42.8MB