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5. Let's dig deeper/2. EDA on Mc'donalds Data Set.mp4 723.3 MB
4. Advance Python/2. Advance Programming in Python Part 2.mp4 629.0 MB
5. Let's dig deeper/3. Exploratory Data Analysis.mp4 460.1 MB
4. Advance Python/1. Advance Programming in Python.mp4 377.0 MB
4. Advance Python/5. Multivariate Plotting.mp4 352.8 MB
7. Module Seven/2. How to use Linear Regression.mp4 318.3 MB
3. Python for Data Science/15. Control Flow in Python.mp4 286.8 MB
4. Advance Python/3. Data Visualisations.mp4 276.4 MB
3. Python for Data Science/17. Types of Functions in Python.mp4 240.0 MB
3. Python for Data Science/9. Data Types & Related Stuffs in Python.mp4 213.1 MB
2. Basic Maths Required for Data Science/8. Probability.mp4 188.5 MB
7. Module Seven/3. Logistic Regression.mp4 163.9 MB
7. Module Seven/1. Linear Regression.mp4 157.4 MB
2. Basic Maths Required for Data Science/4. Measures of Spread.mp4 156.2 MB
4. Advance Python/4. Bivariate Plotting.mp4 156.1 MB
7. Module Seven/4. Logistic Regression on Titanic Data Set.mp4 147.9 MB
10. Project Telecom Churn Production/3. Project Part 3.mp4 135.7 MB
2. Basic Maths Required for Data Science/1. Let's Start with Statistics.mp4 135.2 MB
3. Python for Data Science/14. Operators in Python.mp4 126.5 MB
10. Project Telecom Churn Production/5. Project Let's Finalise it.mp4 125.2 MB
3. Python for Data Science/16. Functions in Python.mp4 122.9 MB
10. Project Telecom Churn Production/4. Project part 4.mp4 119.4 MB
10. Project Telecom Churn Production/1. Project Part 1 Let's get our system ready.mp4 118.1 MB
2. Basic Maths Required for Data Science/11. Normal Probability Distribution.mp4 109.3 MB
2. Basic Maths Required for Data Science/3. Types of Statistics.mp4 96.0 MB
7. Module Seven/24. Model Evaluation recall Curve.mp4 94.7 MB
3. Python for Data Science/18. Argument in a Function.mp4 92.6 MB
2. Basic Maths Required for Data Science/6. Plots Visualisation.mp4 91.7 MB
7. Module Seven/6. Algorithms used in Decision Treee.mp4 91.0 MB
7. Module Seven/10. Working on Titanic Data Set.mp4 88.9 MB
9. Featured Topics in Java/1. Big Data.mp4 88.7 MB
2. Basic Maths Required for Data Science/13. Hypothesis Testing for Decision Making.mp4 87.9 MB
6. Let's Explore in to Machine Learning/1. Introduction Machine Learning.mp4 85.4 MB
3. Python for Data Science/19. Recursive Functions in Python.mp4 82.0 MB
7. Module Seven/18. Linear Regression.mp4 79.9 MB
3. Python for Data Science/12. Output Formatting.mp4 79.3 MB
7. Module Seven/15. Random Forest Implementation on Titanic Data Set.mp4 77.2 MB
1. Introduction/1. Getting Started with Data Science.mp4 75.3 MB
2. Basic Maths Required for Data Science/2. Data Quality Issues.mp4 65.8 MB
8. Module Eight/3. All about R Language.mp4 61.1 MB
9. Featured Topics in Java/2. Intro to Hadoop.mp4 60.8 MB
2. Basic Maths Required for Data Science/5. Measures of Shapes.mp4 60.4 MB
10. Project Telecom Churn Production/2. Project part 2.mp4 60.4 MB
2. Basic Maths Required for Data Science/10. Random Variables.mp4 60.0 MB
9. Featured Topics in Java/3. Intro to Tableu.mp4 59.7 MB
3. Python for Data Science/10. Conversion of Data Types in Python.mp4 56.8 MB
9. Featured Topics in Java/4. Intro to Business Analytics.mp4 56.5 MB
3. Python for Data Science/1. Python for Data Science.mp4 54.8 MB
2. Basic Maths Required for Data Science/12. Central Limit Theorem.mp4 51.8 MB
3. Python for Data Science/20. Lambda or Anonymous Functions in Python.mp4 49.3 MB
2. Basic Maths Required for Data Science/7. Inferential Statistics.mp4 48.2 MB
3. Python for Data Science/8. Variables in Python.mp4 45.7 MB
6. Let's Explore in to Machine Learning/3. Reinforement Learning.mp4 45.2 MB
3. Python for Data Science/5. Comments in Python.mp4 44.5 MB
7. Module Seven/7. Gini Index.mp4 41.0 MB
3. Python for Data Science/6. Python Indentation.mp4 40.2 MB
7. Module Seven/20. Confusion Matrix.mp4 39.0 MB
7. Module Seven/8. Issues with Decision Tree.mp4 38.5 MB
7. Module Seven/23. AUC ROC curve.mp4 38.3 MB
7. Module Seven/22. FB score.mp4 38.0 MB
3. Python for Data Science/2. Python Installation - Google Collab.mp4 37.8 MB
3. Python for Data Science/4. Identifiers in Python.mp4 37.3 MB
3. Python for Data Science/13. User Input in Python.mp4 34.0 MB
7. Module Seven/16. Model Evaluation Technique.mp4 32.7 MB
6. Let's Explore in to Machine Learning/2. Unsupervised Learning.mp4 30.8 MB
5. Let's dig deeper/1. EDA.mp4 28.7 MB
7. Module Seven/5. Decision Tree.mp4 28.3 MB
7. Module Seven/17. Concept of R-Squared.mp4 28.1 MB
2. Basic Maths Required for Data Science/9. Conditional Probability.mp4 27.7 MB
7. Module Seven/11. Random Forest.mp4 27.4 MB
7. Module Seven/21. Recall Sensitivity True Rate of Positive.mp4 24.0 MB
8. Module Eight/2. Data Analysis using R part 2.mp4 23.9 MB
7. Module Seven/19. Classification.mp4 22.7 MB
3. Python for Data Science/3. Python Basics.mp4 21.1 MB
8. Module Eight/1. Data Analysis using R.mp4 20.7 MB
7. Module Seven/14. Application of Random Forest.mp4 18.9 MB
3. Python for Data Science/7. Python Statements.mp4 18.7 MB
3. Python for Data Science/11. Python IO functions.mp4 14.9 MB
7. Module Seven/9. Applications of Decision Tree.mp4 13.7 MB
7. Module Seven/13. Why Random Forest.mp4 10.0 MB
7. Module Seven/12. Types of Random Forest.mp4 3.9 MB
4. Advance Python/2. Advance Programming in Python Part 2.srt 124.4 kB
5. Let's dig deeper/2. EDA on Mc'donalds Data Set.srt 114.7 kB
5. Let's dig deeper/3. Exploratory Data Analysis.srt 79.9 kB
4. Advance Python/1. Advance Programming in Python.srt 74.6 kB
4. Advance Python/5. Multivariate Plotting.srt 63.0 kB
7. Module Seven/2. How to use Linear Regression.srt 58.2 kB
3. Python for Data Science/15. Control Flow in Python.srt 55.8 kB
4. Advance Python/3. Data Visualisations.srt 46.5 kB
7. Module Seven/3. Logistic Regression.srt 41.0 kB
7. Module Seven/1. Linear Regression.srt 38.1 kB
4. Advance Python/4. Bivariate Plotting.srt 37.8 kB
3. Python for Data Science/17. Types of Functions in Python.srt 34.2 kB
3. Python for Data Science/9. Data Types & Related Stuffs in Python.srt 33.4 kB
10. Project Telecom Churn Production/3. Project Part 3.srt 24.6 kB
7. Module Seven/6. Algorithms used in Decision Treee.srt 24.6 kB
3. Python for Data Science/14. Operators in Python.srt 23.2 kB
7. Module Seven/4. Logistic Regression on Titanic Data Set.srt 22.9 kB
10. Project Telecom Churn Production/4. Project part 4.srt 22.2 kB
10. Project Telecom Churn Production/5. Project Let's Finalise it.srt 22.1 kB
3. Python for Data Science/16. Functions in Python.srt 20.8 kB
6. Let's Explore in to Machine Learning/1. Introduction Machine Learning.srt 20.0 kB
10. Project Telecom Churn Production/1. Project Part 1 Let's get our system ready.srt 18.9 kB
7. Module Seven/24. Model Evaluation recall Curve.srt 15.6 kB
9. Featured Topics in Java/1. Big Data.srt 15.0 kB
1. Introduction/1. Getting Started with Data Science.srt 14.2 kB
7. Module Seven/10. Working on Titanic Data Set.srt 14.0 kB
2. Basic Maths Required for Data Science/8. Probability.srt 13.8 kB
2. Basic Maths Required for Data Science/1. Let's Start with Statistics.srt 13.6 kB
9. Featured Topics in Java/3. Intro to Tableu.srt 12.8 kB
7. Module Seven/15. Random Forest Implementation on Titanic Data Set.srt 12.6 kB
3. Python for Data Science/19. Recursive Functions in Python.srt 12.4 kB
2. Basic Maths Required for Data Science/4. Measures of Spread.srt 12.3 kB
3. Python for Data Science/18. Argument in a Function.srt 12.3 kB
9. Featured Topics in Java/4. Intro to Business Analytics.srt 12.2 kB
7. Module Seven/7. Gini Index.srt 12.1 kB
8. Module Eight/3. All about R Language.srt 12.1 kB
7. Module Seven/18. Linear Regression.srt 11.8 kB
2. Basic Maths Required for Data Science/11. Normal Probability Distribution.srt 11.5 kB
7. Module Seven/22. FB score.srt 11.3 kB
9. Featured Topics in Java/2. Intro to Hadoop.srt 10.8 kB
10. Project Telecom Churn Production/2. Project part 2.srt 10.6 kB
5. Let's dig deeper/1. EDA.srt 10.0 kB
2. Basic Maths Required for Data Science/6. Plots Visualisation.srt 9.8 kB
2. Basic Maths Required for Data Science/3. Types of Statistics.srt 9.7 kB
6. Let's Explore in to Machine Learning/3. Reinforement Learning.srt 9.2 kB
2. Basic Maths Required for Data Science/13. Hypothesis Testing for Decision Making.srt 9.1 kB
7. Module Seven/20. Confusion Matrix.srt 8.8 kB
7. Module Seven/8. Issues with Decision Tree.srt 8.7 kB
7. Module Seven/23. AUC ROC curve.srt 8.7 kB
7. Module Seven/16. Model Evaluation Technique.srt 8.1 kB
3. Python for Data Science/2. Python Installation - Google Collab.srt 7.9 kB
3. Python for Data Science/10. Conversion of Data Types in Python.srt 7.6 kB
7. Module Seven/5. Decision Tree.srt 7.1 kB
3. Python for Data Science/20. Lambda or Anonymous Functions in Python.srt 7.0 kB
6. Let's Explore in to Machine Learning/2. Unsupervised Learning.srt 6.9 kB
2. Basic Maths Required for Data Science/5. Measures of Shapes.srt 6.8 kB
3. Python for Data Science/8. Variables in Python.srt 6.5 kB
3. Python for Data Science/5. Comments in Python.srt 6.4 kB
3. Python for Data Science/12. Output Formatting.srt 6.4 kB
3. Python for Data Science/6. Python Indentation.srt 6.4 kB
2. Basic Maths Required for Data Science/10. Random Variables.srt 6.2 kB
2. Basic Maths Required for Data Science/2. Data Quality Issues.srt 6.1 kB
7. Module Seven/11. Random Forest.srt 6.0 kB
7. Module Seven/19. Classification.srt 5.9 kB
7. Module Seven/17. Concept of R-Squared.srt 5.9 kB
2. Basic Maths Required for Data Science/7. Inferential Statistics.srt 5.4 kB
7. Module Seven/21. Recall Sensitivity True Rate of Positive.srt 5.4 kB
3. Python for Data Science/1. Python for Data Science.srt 5.1 kB
8. Module Eight/2. Data Analysis using R part 2.srt 4.9 kB
2. Basic Maths Required for Data Science/12. Central Limit Theorem.srt 4.8 kB
8. Module Eight/1. Data Analysis using R.srt 4.4 kB
3. Python for Data Science/4. Identifiers in Python.srt 4.4 kB
3. Python for Data Science/7. Python Statements.srt 4.2 kB
3. Python for Data Science/3. Python Basics.srt 3.5 kB
7. Module Seven/14. Application of Random Forest.srt 3.4 kB
2. Basic Maths Required for Data Science/9. Conditional Probability.srt 3.0 kB
7. Module Seven/9. Applications of Decision Tree.srt 3.0 kB
3. Python for Data Science/13. User Input in Python.srt 2.6 kB
7. Module Seven/13. Why Random Forest.srt 2.5 kB
3. Python for Data Science/11. Python IO functions.srt 1.7 kB
7. Module Seven/12. Types of Random Forest.srt 1.5 kB
1. Introduction/[Tutorialsplanet.NET].url 128 Bytes
10. Project Telecom Churn Production/[Tutorialsplanet.NET].url 128 Bytes
5. Let's dig deeper/[Tutorialsplanet.NET].url 128 Bytes
8. Module Eight/[Tutorialsplanet.NET].url 128 Bytes
[Tutorialsplanet.NET].url 128 Bytes

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