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种子名称： [FreeCourseSite.com] Udemy - Data Science with Python Complete Course
文件类型： 视频
文件数目： 81个文件
文件大小： 8.08 GB
收录时间： 2023-6-11 00:07
已经下载： 3次
资源热度： 132
最近下载： 2024-6-11 03:02

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[FreeCourseSite.com] Udemy - Data Science with Python Complete Course.torrent

1. Introduction/1. Getting Started with Data Science.mp471.82MB

10. Project Telecom Churn Production/1. Project Part 1 Let's get our system ready.mp4112.64MB

10. Project Telecom Churn Production/2. Project part 2.mp457.63MB

10. Project Telecom Churn Production/3. Project Part 3.mp4129.4MB

10. Project Telecom Churn Production/4. Project part 4.mp4113.85MB

10. Project Telecom Churn Production/5. Project Let's Finalise it.mp4119.43MB

2. Basic Maths Required for Data Science/1. Let's Start with Statistics.mp4128.91MB

2. Basic Maths Required for Data Science/10. Random Variables.mp457.22MB

2. Basic Maths Required for Data Science/11. Normal Probability Distribution.mp4104.21MB

2. Basic Maths Required for Data Science/12. Central Limit Theorem.mp449.37MB

2. Basic Maths Required for Data Science/13. Hypothesis Testing for Decision Making.mp483.86MB

2. Basic Maths Required for Data Science/2. Data Quality Issues.mp462.75MB

2. Basic Maths Required for Data Science/3. Types of Statistics.mp491.56MB

2. Basic Maths Required for Data Science/4. Measures of Spread.mp4148.93MB

2. Basic Maths Required for Data Science/5. Measures of Shapes.mp457.64MB

2. Basic Maths Required for Data Science/6. Plots Visualisation.mp487.49MB

2. Basic Maths Required for Data Science/7. Inferential Statistics.mp445.98MB

2. Basic Maths Required for Data Science/8. Probability.mp4179.78MB

2. Basic Maths Required for Data Science/9. Conditional Probability.mp426.46MB

3. Python for Data Science/1. Python for Data Science.mp452.24MB

3. Python for Data Science/10. Conversion of Data Types in Python.mp454.2MB

3. Python for Data Science/11. Python IO functions.mp414.24MB

3. Python for Data Science/12. Output Formatting.mp475.6MB

3. Python for Data Science/13. User Input in Python.mp432.47MB

3. Python for Data Science/14. Operators in Python.mp4120.69MB

3. Python for Data Science/15. Control Flow in Python.mp4273.5MB

3. Python for Data Science/16. Functions in Python.mp4117.24MB

3. Python for Data Science/17. Types of Functions in Python.mp4228.92MB

3. Python for Data Science/18. Argument in a Function.mp488.29MB

3. Python for Data Science/19. Recursive Functions in Python.mp478.22MB

3. Python for Data Science/2. Python Installation - Google Collab.mp436.01MB

3. Python for Data Science/20. Lambda or Anonymous Functions in Python.mp447.04MB

3. Python for Data Science/3. Python Basics.mp420.12MB

3. Python for Data Science/4. Identifiers in Python.mp435.57MB

3. Python for Data Science/5. Comments in Python.mp442.41MB

3. Python for Data Science/6. Python Indentation.mp438.34MB

3. Python for Data Science/7. Python Statements.mp417.82MB

3. Python for Data Science/8. Variables in Python.mp443.55MB

3. Python for Data Science/9. Data Types & Related Stuffs in Python.mp4203.27MB

4. Advance Python/1. Advance Programming in Python.mp4359.55MB

4. Advance Python/2. Advance Programming in Python Part 2.mp4599.88MB

4. Advance Python/3. Data Visualisations.mp4263.61MB

4. Advance Python/4. Bivariate Plotting.mp4148.84MB

4. Advance Python/5. Multivariate Plotting.mp4336.46MB

5. Let's dig deeper/1. EDA.mp427.41MB

5. Let's dig deeper/2. EDA on Mc'donalds Data Set.mp4689.8MB

5. Let's dig deeper/3. Exploratory Data Analysis.mp4438.81MB

6. Let's Explore in to Machine Learning/1. Introduction Machine Learning.mp481.42MB

6. Let's Explore in to Machine Learning/2. Unsupervised Learning.mp429.34MB

6. Let's Explore in to Machine Learning/3. Reinforement Learning.mp443.08MB

7. Module Seven/1. Linear Regression.mp4150.1MB

7. Module Seven/10. Working on Titanic Data Set.mp484.75MB

7. Module Seven/11. Random Forest.mp426.1MB

7. Module Seven/12. Types of Random Forest.mp43.76MB

7. Module Seven/13. Why Random Forest.mp49.53MB

7. Module Seven/14. Application of Random Forest.mp417.99MB

7. Module Seven/15. Random Forest Implementation on Titanic Data Set.mp473.59MB

7. Module Seven/16. Model Evaluation Technique.mp431.14MB

7. Module Seven/17. Concept of R-Squared.mp426.84MB

7. Module Seven/18. Linear Regression.mp476.2MB

7. Module Seven/19. Classification.mp421.68MB

7. Module Seven/2. How to use Linear Regression.mp4303.55MB

7. Module Seven/20. Confusion Matrix.mp437.17MB

7. Module Seven/21. Recall Sensitivity True Rate of Positive.mp422.85MB

7. Module Seven/22. FB score.mp436.24MB

7. Module Seven/23. AUC ROC curve.mp436.48MB

7. Module Seven/24. Model Evaluation recall Curve.mp490.3MB

7. Module Seven/3. Logistic Regression.mp4156.34MB

7. Module Seven/4. Logistic Regression on Titanic Data Set.mp4141.05MB

7. Module Seven/5. Decision Tree.mp427.03MB

7. Module Seven/6. Algorithms used in Decision Treee.mp486.75MB

7. Module Seven/7. Gini Index.mp439.13MB

7. Module Seven/8. Issues with Decision Tree.mp436.7MB

7. Module Seven/9. Applications of Decision Tree.mp413.06MB

8. Module Eight/1. Data Analysis using R.mp419.78MB

8. Module Eight/2. Data Analysis using R part 2.mp422.74MB

8. Module Eight/3. All about R Language.mp458.24MB

9. Featured Topics in Java/1. Big Data.mp484.59MB

9. Featured Topics in Java/2. Intro to Hadoop.mp457.96MB

9. Featured Topics in Java/3. Intro to Tableu.mp456.97MB

9. Featured Topics in Java/4. Intro to Business Analytics.mp453.86MB