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[GigaCourse Com] Udemy - Machine Learning in Python with 5 Machine Learning Projects

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Report [GigaCourse Com] Udemy - Machine Learning in Python with 5 Machine Learning Projects
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Title: UdemyGroup: NOGRPSource: Udemy
Info Hash
98F5357292CB5751E089A270D7C1E4570D0CF166
Source
Unverified
Total Size
20.82 GB
Total Files
100
Seeders
0
Leechers
3
Health
Score
3
Type
Bookware

File List

FileSize
0. Websites you may like/[CourseClub.Me].url122 B
0. Websites you may like/[GigaCourse.Com].url49 B
1. Python Fundamentals/1. Why should you learn Python.mp465.68 MB
1. Python Fundamentals/1. Why should you learn Python.srt3.33 KB
1. Python Fundamentals/10. Identity and Membership Operators.mp439.22 MB
1. Python Fundamentals/10. Identity and Membership Operators.srt2.84 KB
1. Python Fundamentals/11. Quiz on Operators.html147 B
1. Python Fundamentals/12. Quiz Solution.mp434.21 MB
1. Python Fundamentals/12. Quiz Solution.srt4.01 KB
1. Python Fundamentals/13. String Formatting.mp451.35 MB
1. Python Fundamentals/13. String Formatting.srt4.8 KB
1. Python Fundamentals/14. String Methods.mp443.29 MB
1. Python Fundamentals/14. String Methods.srt5.93 KB
1. Python Fundamentals/15. User Input.mp441.04 MB
1. Python Fundamentals/15. User Input.srt2.88 KB
1. Python Fundamentals/16. Quiz on Strings.html147 B
1. Python Fundamentals/17. Quiz Solution.mp453.11 MB
1. Python Fundamentals/17. Quiz Solution.srt4.35 KB
1. Python Fundamentals/18. If, elif, and else.mp465.9 MB
1. Python Fundamentals/18. If, elif, and else.srt3.75 KB
1. Python Fundamentals/19. For and While.mp453.07 MB
1. Python Fundamentals/19. For and While.srt5.26 KB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp433.48 MB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.srt2.44 KB
1. Python Fundamentals/20. Break and Continue.mp440.72 MB
1. Python Fundamentals/20. Break and Continue.srt2.86 KB
1. Python Fundamentals/21. Quiz on Loops and Conditionals.html147 B
1. Python Fundamentals/22. Quiz Solution.mp449.04 MB
1. Python Fundamentals/22. Quiz Solution.srt4.28 KB
1. Python Fundamentals/3. Naming Convention for Variables.mp4102.24 MB
1. Python Fundamentals/3. Naming Convention for Variables.srt6.05 KB
1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4119.86 MB
1. Python Fundamentals/4. Built in Data Types and Type Casting.srt6.54 KB
1. Python Fundamentals/5. Scope of Variables.mp477.16 MB
1. Python Fundamentals/5. Scope of Variables.srt4.1 KB
1. Python Fundamentals/6. Quiz on Variables and Data Types.html147 B
1. Python Fundamentals/7. Quiz Solution.mp446.52 MB
1. Python Fundamentals/7. Quiz Solution.srt5.29 KB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp478.04 MB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.srt7.98 KB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp462.41 MB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.srt7.46 KB
10. Logistic Regression/1. Introduction to Logistic Regression.mp4106.4 MB
10. Logistic Regression/1. Introduction to Logistic Regression.srt6.83 KB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp459.89 MB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.srt3.34 KB
10. Logistic Regression/11. Quiz on Modelling with Logistic Regression.html147 B
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp487.01 MB
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.srt9.17 KB
10. Logistic Regression/3. Feature Selection using RFECV.mp442.15 MB
10. Logistic Regression/3. Feature Selection using RFECV.srt3 KB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp458.75 MB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.srt5 KB
10. Logistic Regression/5. Applying Cross Validation.mp456.73 MB
10. Logistic Regression/5. Applying Cross Validation.srt3.81 KB
10. Logistic Regression/6. How to analyze performance of a classification model.mp4146.18 MB
10. Logistic Regression/6. How to analyze performance of a classification model.srt8.73 KB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp455.54 MB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.srt4.65 KB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4147.63 MB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.srt9.47 KB
10. Logistic Regression/9. Real time prediction using logistic regression.mp474.65 MB
10. Logistic Regression/9. Real time prediction using logistic regression.srt7.01 KB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4108.17 MB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.srt5.76 KB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp470.38 MB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.srt3.77 KB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp467.43 MB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.srt7.44 KB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4104.32 MB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.srt5.39 KB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp433.23 MB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.srt2.01 KB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4174.72 MB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.srt10.41 KB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp461.96 MB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.srt3.41 KB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp469.77 MB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.srt3.98 KB
11. Introduction to KNN, SVM, Naive Bayes/9. Quiz on Other classification models.html147 B
12. Tree Based Models/1. Intuition for decision trees.mp481.99 MB
12. Tree Based Models/1. Intuition for decision trees.srt4.56 KB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4218.66 MB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.srt13.24 KB
12. Tree Based Models/3. Advantages and Issues with Decision trees.mp453.37 MB
12. Tree Based Models/3. Advantages and Issues with Decision trees.srt2.97 KB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp435.8 MB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.srt3.62 KB
12. Tree Based Models/5. Understanding the concept of Bagging.mp465.99 MB
12. Tree Based Models/5. Understanding the concept of Bagging.srt3.62 KB
12. Tree Based Models/6. Introduction to Random forest.mp468.09 MB
12. Tree Based Models/6. Introduction to Random forest.srt3.97 KB
12. Tree Based Models/7. Understanding the parameters of Random forest.mp453.66 MB
12. Tree Based Models/7. Understanding the parameters of Random forest.srt4.27 KB
12. Tree Based Models/8. Implementing random forest using Sklearn.mp447.88 MB
12. Tree Based Models/8. Implementing random forest using Sklearn.srt4.35 KB
12. Tree Based Models/9. Quiz on Tree based models.html147 B
13. Boosting Models/1. Understading the concept of boosting.mp457.14 MB
13. Boosting Models/1. Understading the concept of boosting.srt3.04 KB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4153.3 MB

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