GetFreeCourses.Co-Udemy-Machine Learning & Data Science A-Z Hands-on Python 2021

磁力链接/BT种子名称

GetFreeCourses.Co-Udemy-Machine Learning & Data Science A-Z Hands-on Python 2021

磁力链接/BT种子简介

种子哈希：010a2d06b48e96164b9085997f682f486bf13ab1
文件大小： 6.74G
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收录时间：2022-04-20
最近下载：2025-02-15

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文件列表

  • 6. Supervised Learning - Regression/8. Random Forest Model Development.mp4 258.2 MB
  • 5. Supervised Learning - Classification/1. Supervised Learning Models - Introduction and Understanding the Data.mp4 245.1 MB
  • 5. Supervised Learning - Classification/4. k-NN Training-Set and Test-Set Creation.mp4 239.5 MB
  • 3. Data Preprocessing/6. Missing Values2.mp4 230.0 MB
  • 6. Supervised Learning - Regression/1. Simple and Multiple Linear Regression Concepts.mp4 222.5 MB
  • 3. Data Preprocessing/3. Statistics2.mp4 217.6 MB
  • 6. Supervised Learning - Regression/6. Polynomial Linear Regression Model Development.mp4 217.0 MB
  • 2. Machine Learning Useful Packages (Libraries)/13. Visualization with Matplotlib2.mp4 215.2 MB
  • 2. Machine Learning Useful Packages (Libraries)/11. Pandas4.mp4 212.9 MB
  • 2. Machine Learning Useful Packages (Libraries)/14. Visualization with Matplotlib3.mp4 198.0 MB
  • 3. Data Preprocessing/12. Normalization.mp4 195.9 MB
  • 5. Supervised Learning - Classification/13. Model Evaluation - Calculating with Python.mp4 182.5 MB
  • 6. Supervised Learning - Regression/4. Evaluation Metrics - Implementation.mp4 167.7 MB
  • 3. Data Preprocessing/1. Reading and Modifying a Dataset.mp4 162.1 MB
  • 2. Machine Learning Useful Packages (Libraries)/6. NumPy5.mp4 160.1 MB
  • 7. Unsupervised Learning - Clustering Techniques/10. Hierarchical Clustering Model Development.mp4 153.0 MB
  • 2. Machine Learning Useful Packages (Libraries)/15. Visualization with Matplotlib4.mp4 149.9 MB
  • 5. Supervised Learning - Classification/3. k-NN Model Development.mp4 147.5 MB
  • 2. Machine Learning Useful Packages (Libraries)/7. NumPy6.mp4 141.0 MB
  • 8. Hyper Parameter Optimization (Model Tuning)/4. k-NN - Model Tuning.mp4 140.1 MB
  • 3. Data Preprocessing/8. Outlier Detection2.mp4 137.0 MB
  • 3. Data Preprocessing/5. Missing Values1.mp4 135.9 MB
  • 2. Machine Learning Useful Packages (Libraries)/16. Visualization with Matplotlib5.mp4 135.5 MB
  • 8. Hyper Parameter Optimization (Model Tuning)/2. Support Vector Regression - Model Tuning.mp4 131.7 MB
  • 6. Supervised Learning - Regression/10. Support Vector Regression Model Development.mp4 126.9 MB
  • 2. Machine Learning Useful Packages (Libraries)/10. Pandas3.mp4 123.6 MB
  • 2. Machine Learning Useful Packages (Libraries)/9. Pandas2.mp4 122.6 MB
  • 5. Supervised Learning - Classification/11. Logistic Regression Model Development.mp4 117.6 MB
  • 3. Data Preprocessing/4. Statistics3 - Covariance.mp4 112.7 MB
  • 7. Unsupervised Learning - Clustering Techniques/5. K-means Model Development2.mp4 108.9 MB
  • 7. Unsupervised Learning - Clustering Techniques/6. K-means - Model Evaluation.mp4 107.4 MB
  • 2. Machine Learning Useful Packages (Libraries)/12. Visualization with Matplotlib1.mp4 104.3 MB
  • 2. Machine Learning Useful Packages (Libraries)/8. Pandas1.mp4 100.3 MB
  • 7. Unsupervised Learning - Clustering Techniques/8. DBSCAN Model Development.mp4 91.1 MB
  • 2. Machine Learning Useful Packages (Libraries)/4. NumPy3.mp4 88.6 MB
  • 5. Supervised Learning - Classification/12. Model Evaluation Concepts.mp4 87.5 MB
  • 6. Supervised Learning - Regression/2. Multiple Linear Regression - Model Development.mp4 79.3 MB
  • 3. Data Preprocessing/7. Outlier Detection1.mp4 76.8 MB
  • 8. Hyper Parameter Optimization (Model Tuning)/5. Overfitting and Underfitting.mp4 75.6 MB
  • 1. Introduction/6. Installation of Required Libraries.mp4 74.2 MB
  • 5. Supervised Learning - Classification/6. Decision Tree Model Development.mp4 70.1 MB
  • 3. Data Preprocessing/10. Concatenation.mp4 69.1 MB
  • 5. Supervised Learning - Classification/8. Naive Bayes Concepts.mp4 62.1 MB
  • 5. Supervised Learning - Classification/9. Naive Bayes Model Development.mp4 61.8 MB
  • 3. Data Preprocessing/11. Dummy Variable.mp4 60.4 MB
  • 2. Machine Learning Useful Packages (Libraries)/3. NumPy2.mp4 59.7 MB
  • 2. Machine Learning Useful Packages (Libraries)/5. NumPy4.mp4 59.3 MB
  • 5. Supervised Learning - Classification/7. Decision Tree - Cross Validation.mp4 57.3 MB
  • 6. Supervised Learning - Regression/3. Evaluation Metrics - Concepts.mp4 51.9 MB
  • 5. Supervised Learning - Classification/2. k-NN Concepts.mp4 50.4 MB
  • 1. Introduction/7. Spyder Interface.mp4 48.6 MB
  • 4. Machine Learning Introduction/1. Learning Types.mp4 47.6 MB
  • 7. Unsupervised Learning - Clustering Techniques/2. K-means Concepts1.mp4 46.7 MB
  • 7. Unsupervised Learning - Clustering Techniques/1. Introduction.mp4 40.0 MB
  • 2. Machine Learning Useful Packages (Libraries)/2. NumPy1.mp4 39.3 MB
  • 7. Unsupervised Learning - Clustering Techniques/4. K-means Model Development1.mp4 37.7 MB
  • 3. Data Preprocessing/2. Statistics1.mp4 35.7 MB
  • 3. Data Preprocessing/9. Outlier Detection3.mp4 32.5 MB
  • 6. Supervised Learning - Regression/7. Random Forest Concepts.mp4 31.7 MB
  • 6. Supervised Learning - Regression/9. Support Vector Regression Concepts.mp4 28.3 MB
  • 7. Unsupervised Learning - Clustering Techniques/7. DBSCAN Concepts.mp4 28.2 MB
  • 6. Supervised Learning - Regression/5. Polynomial Linear Regression Concepts.mp4 27.7 MB
  • 1. Introduction/2. What is Machine Learning Some Basic Terms.mp4 27.1 MB
  • 5. Supervised Learning - Classification/5. Decision Tree Concepts.mp4 26.9 MB
  • 7. Unsupervised Learning - Clustering Techniques/9. Hierarchical Clustering Concepts.mp4 25.5 MB
  • 1. Introduction/5. IDE Installation.mp4 23.4 MB
  • 7. Unsupervised Learning - Clustering Techniques/3. K-means Concepts2.mp4 22.3 MB
  • 1. Introduction/1. Course Content.mp4 17.9 MB
  • 8. Hyper Parameter Optimization (Model Tuning)/1. Introduction.mp4 17.9 MB
  • 8. Hyper Parameter Optimization (Model Tuning)/3. K-Means - Model Tuning.mp4 16.0 MB
  • 5. Supervised Learning - Classification/10. Logistic Regression Concepts.mp4 11.4 MB
  • 1. Introduction/4. Python IDE.mp4 7.9 MB
  • 5. Supervised Learning - Classification/1. Supervised Learning Models - Introduction and Understanding the Data.srt 33.9 kB
  • 6. Supervised Learning - Regression/1. Simple and Multiple Linear Regression Concepts.srt 32.0 kB
  • 5. Supervised Learning - Classification/4. k-NN Training-Set and Test-Set Creation.srt 28.4 kB
  • 2. Machine Learning Useful Packages (Libraries)/11. Pandas4.srt 27.3 kB
  • 2. Machine Learning Useful Packages (Libraries)/13. Visualization with Matplotlib2.srt 26.3 kB
  • 6. Supervised Learning - Regression/8. Random Forest Model Development.srt 25.2 kB
  • 3. Data Preprocessing/6. Missing Values2.srt 22.9 kB
  • 3. Data Preprocessing/1. Reading and Modifying a Dataset.srt 22.7 kB
  • 3. Data Preprocessing/12. Normalization.srt 22.4 kB
  • 3. Data Preprocessing/3. Statistics2.srt 22.4 kB
  • 6. Supervised Learning - Regression/6. Polynomial Linear Regression Model Development.srt 21.2 kB
  • 5. Supervised Learning - Classification/13. Model Evaluation - Calculating with Python.srt 20.4 kB
  • 2. Machine Learning Useful Packages (Libraries)/14. Visualization with Matplotlib3.srt 20.1 kB
  • 5. Supervised Learning - Classification/12. Model Evaluation Concepts.srt 19.8 kB
  • 2. Machine Learning Useful Packages (Libraries)/6. NumPy5.srt 19.6 kB
  • 2. Machine Learning Useful Packages (Libraries)/7. NumPy6.srt 18.9 kB
  • 6. Supervised Learning - Regression/4. Evaluation Metrics - Implementation.srt 18.7 kB
  • 7. Unsupervised Learning - Clustering Techniques/10. Hierarchical Clustering Model Development.srt 18.1 kB
  • 2. Machine Learning Useful Packages (Libraries)/9. Pandas2.srt 17.7 kB
  • 2. Machine Learning Useful Packages (Libraries)/8. Pandas1.srt 17.7 kB
  • 2. Machine Learning Useful Packages (Libraries)/15. Visualization with Matplotlib4.srt 17.7 kB
  • 2. Machine Learning Useful Packages (Libraries)/10. Pandas3.srt 17.0 kB
  • 5. Supervised Learning - Classification/3. k-NN Model Development.srt 16.9 kB
  • 5. Supervised Learning - Classification/8. Naive Bayes Concepts.srt 16.7 kB
  • 2. Machine Learning Useful Packages (Libraries)/12. Visualization with Matplotlib1.srt 16.4 kB
  • 3. Data Preprocessing/4. Statistics3 - Covariance.srt 16.1 kB
  • 3. Data Preprocessing/8. Outlier Detection2.srt 15.5 kB
  • 2. Machine Learning Useful Packages (Libraries)/16. Visualization with Matplotlib5.srt 14.8 kB
  • 3. Data Preprocessing/5. Missing Values1.srt 14.8 kB
  • 7. Unsupervised Learning - Clustering Techniques/5. K-means Model Development2.srt 14.1 kB
  • 2. Machine Learning Useful Packages (Libraries)/4. NumPy3.srt 13.9 kB
  • 8. Hyper Parameter Optimization (Model Tuning)/2. Support Vector Regression - Model Tuning.srt 13.9 kB
  • 3. Data Preprocessing/7. Outlier Detection1.srt 13.6 kB
  • 8. Hyper Parameter Optimization (Model Tuning)/4. k-NN - Model Tuning.srt 13.4 kB
  • 6. Supervised Learning - Regression/3. Evaluation Metrics - Concepts.srt 12.9 kB
  • 5. Supervised Learning - Classification/11. Logistic Regression Model Development.srt 12.2 kB
  • 7. Unsupervised Learning - Clustering Techniques/6. K-means - Model Evaluation.srt 11.8 kB
  • 5. Supervised Learning - Classification/2. k-NN Concepts.srt 11.8 kB
  • 6. Supervised Learning - Regression/10. Support Vector Regression Model Development.srt 11.7 kB
  • 7. Unsupervised Learning - Clustering Techniques/2. K-means Concepts1.srt 11.5 kB
  • 8. Hyper Parameter Optimization (Model Tuning)/5. Overfitting and Underfitting.srt 11.5 kB
  • 7. Unsupervised Learning - Clustering Techniques/8. DBSCAN Model Development.srt 10.7 kB
  • 3. Data Preprocessing/2. Statistics1.srt 10.4 kB
  • 2. Machine Learning Useful Packages (Libraries)/3. NumPy2.srt 9.9 kB
  • 5. Supervised Learning - Classification/7. Decision Tree - Cross Validation.srt 9.9 kB
  • 1. Introduction/7. Spyder Interface.srt 9.2 kB
  • 4. Machine Learning Introduction/1. Learning Types.srt 9.1 kB
  • 6. Supervised Learning - Regression/2. Multiple Linear Regression - Model Development.srt 8.9 kB
  • 1. Introduction/6. Installation of Required Libraries.srt 8.8 kB
  • 7. Unsupervised Learning - Clustering Techniques/1. Introduction.srt 8.5 kB
  • 2. Machine Learning Useful Packages (Libraries)/2. NumPy1.srt 8.4 kB
  • 3. Data Preprocessing/10. Concatenation.srt 8.2 kB
  • 3. Data Preprocessing/11. Dummy Variable.srt 8.1 kB
  • 2. Machine Learning Useful Packages (Libraries)/5. NumPy4.srt 7.9 kB
  • 5. Supervised Learning - Classification/5. Decision Tree Concepts.srt 7.8 kB
  • 6. Supervised Learning - Regression/9. Support Vector Regression Concepts.srt 7.7 kB
  • 2. Machine Learning Useful Packages (Libraries)/1.1 Python Source Codes.zip 7.6 kB
  • 6. Supervised Learning - Regression/7. Random Forest Concepts.srt 7.6 kB
  • 7. Unsupervised Learning - Clustering Techniques/3. K-means Concepts2.srt 7.3 kB
  • 1. Introduction/2. What is Machine Learning Some Basic Terms.srt 7.3 kB
  • 5. Supervised Learning - Classification/6. Decision Tree Model Development.srt 7.2 kB
  • 5. Supervised Learning - Classification/9. Naive Bayes Model Development.srt 7.0 kB
  • 6. Supervised Learning - Regression/5. Polynomial Linear Regression Concepts.srt 6.8 kB
  • 7. Unsupervised Learning - Clustering Techniques/9. Hierarchical Clustering Concepts.srt 6.7 kB
  • 1. Introduction/1. Course Content.srt 6.6 kB
  • 7. Unsupervised Learning - Clustering Techniques/7. DBSCAN Concepts.srt 6.4 kB
  • 7. Unsupervised Learning - Clustering Techniques/4. K-means Model Development1.srt 5.3 kB
  • 8. Hyper Parameter Optimization (Model Tuning)/1. Introduction.srt 4.8 kB
  • 3. Data Preprocessing/9. Outlier Detection3.srt 3.6 kB
  • 5. Supervised Learning - Classification/10. Logistic Regression Concepts.srt 3.5 kB
  • 1. Introduction/5. IDE Installation.srt 3.3 kB
  • 1. Introduction/4. Python IDE.srt 2.8 kB
  • 8. Hyper Parameter Optimization (Model Tuning)/3. K-Means - Model Tuning.srt 2.6 kB
  • 1. Introduction/3. Python Installation.html 612 Bytes
  • 2. Machine Learning Useful Packages (Libraries)/11.1 Data_Set.csv 580 Bytes
  • 3. Data Preprocessing/1.1 Data_Set.csv 580 Bytes
  • 2. Machine Learning Useful Packages (Libraries)/1. Python Source Codes.html 368 Bytes
  • 3. Data Preprocessing/10.1 Data_New.csv 201 Bytes
  • 2. Machine Learning Useful Packages (Libraries)/17. Chapter 2 Quiz.html 160 Bytes
  • 3. Data Preprocessing/13. Chapter3 Quiz.html 160 Bytes
  • 4. Machine Learning Introduction/2. Chapter 4 Quiz.html 160 Bytes
  • 5. Supervised Learning - Classification/14. Chapter 5 Quiz.html 160 Bytes
  • 6. Supervised Learning - Regression/11. Chapter 6 Quiz.html 160 Bytes
  • 7. Unsupervised Learning - Clustering Techniques/11. Chapter 7 Quiz.html 160 Bytes
  • 3. Data Preprocessing/GetFreeCourses.Co.url 116 Bytes
  • 5. Supervised Learning - Classification/GetFreeCourses.Co.url 116 Bytes
  • Download Paid Udemy Courses For Free.url 116 Bytes
  • GetFreeCourses.Co.url 116 Bytes

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