[Tutorialsplanet.NET] Udemy - Probability for Statistics and Data Science

磁力链接/BT种子名称

[Tutorialsplanet.NET] Udemy - Probability for Statistics and Data Science

磁力链接/BT种子简介

种子哈希：fdac039c397d59395b66ed0b8da095f571055805
文件大小： 2.48G
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收录时间：2021-03-16
最近下载：2025-08-24

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文件列表

  • 4. Distributions/29. Practical Example Distributions.mp4 165.1 MB
  • 3. Bayesian Inference/22. Practical Example Bayesian Inference.mp4 152.0 MB
  • 2. Combinatorics/20. Practical Example Combinatorics.mp4 140.9 MB
  • 5. Tie-ins to Other Fields/1. Tie-ins to Finance.mp4 103.6 MB
  • 4. Distributions/3. What are the two main types of distributions based on the type of data we have.mp4 96.1 MB
  • 1. Introduction to Probability/2. What is the probability formula.mp4 90.0 MB
  • 4. Distributions/15. What is a Continuous Distribution.mp4 88.2 MB
  • 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.mp4 80.9 MB
  • 1. Introduction to Probability/4. How to compute expected values.mp4 79.4 MB
  • 4. Distributions/1. What is a probability distribution.mp4 76.9 MB
  • 4. Distributions/11. What is the Binomial Distribution.mp4 72.2 MB
  • 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.mp4 66.5 MB
  • 1. Introduction to Probability/6. What is a probability frequency distribution.mp4 64.6 MB
  • 1. Introduction to Probability/8. What is a complement.mp4 62.0 MB
  • 2. Combinatorics/11. What are combinations and how are they similar to variations.mp4 60.1 MB
  • 3. Bayesian Inference/7. What is the union of sets A and B.mp4 60.0 MB
  • 4. Distributions/13. What is the Poisson Distribution.mp4 58.5 MB
  • 1. Introduction to Probability/1. What does the course cover.mp4 55.2 MB
  • 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.mp4 52.4 MB
  • 4. Distributions/27. What is the Logistic Distribution.mp4 52.4 MB
  • 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.mp4 51.4 MB
  • 4. Distributions/19. Standardizing a Normal Distribution.mp4 50.2 MB
  • 3. Bayesian Inference/3. What are the different ways two events can interact with one another.mp4 49.7 MB
  • 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).mp4 48.1 MB
  • 3. Bayesian Inference/1. What is a set.mp4 47.7 MB
  • 4. Distributions/17. What is a Normal Distribution.mp4 45.9 MB
  • 2. Combinatorics/9. What if we couldn't use certain values more than once.mp4 45.2 MB
  • 2. Combinatorics/3. When do we use Permutations.mp4 43.5 MB
  • 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.mp4 43.3 MB
  • 2. Combinatorics/13. What is symmetry in Combinations.mp4 42.2 MB
  • 4. Distributions/25. What is an Exponential Distribution.mp4 42.1 MB
  • 2. Combinatorics/19. A Summary of Combinatorics.mp4 40.2 MB
  • 2. Combinatorics/5. Solving Factorials.mp4 37.9 MB
  • 3. Bayesian Inference/15. Conditional Probability in Real-Life.mp4 36.6 MB
  • 3. Bayesian Inference/11. What does it mean to for two events to be dependent.mp4 36.5 MB
  • 4. Distributions/9. What is the Bernoulli Distribution.mp4 35.8 MB
  • 2. Combinatorics/7. Why can we use certain values more than once.mp4 35.6 MB
  • 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.mp4 34.6 MB
  • 3. Bayesian Inference/16. How do we apply the additive rule.mp4 28.3 MB
  • 3. Bayesian Inference/5. What is the intersection of sets A and B.mp4 28.2 MB
  • 4. Distributions/23. What is a Chi Squared Distribution.mp4 27.6 MB
  • 3. Bayesian Inference/9. Are all complements mutually exclusive.mp4 26.6 MB
  • 4. Distributions/7. What is the Discrete Uniform Distribution.mp4 25.6 MB
  • 4. Distributions/5. Discrete Distributions and their characteristics..mp4 23.8 MB
  • 4. Distributions/21. What is a Student's T Distribution.mp4 23.1 MB
  • 2. Combinatorics/1. Why are combinatorics useful.mp4 17.0 MB
  • 4. Distributions/29.3 FIFA19 (post).csv 9.1 MB
  • 4. Distributions/29.4 FIFA19.csv 9.1 MB
  • 3. Bayesian Inference/22.1 CDS_2017-2018 Hamilton.pdf 865.6 kB
  • 4. Distributions/1.1 Course Notes - Probability Distributions.pdf 458.8 kB
  • 3. Bayesian Inference/1.1 Section 3 Course Notes.pdf 395.3 kB
  • 1. Introduction to Probability/2.1 Section 1 Course Notes.pdf 380.0 kB
  • 4. Distributions/15.1 Solving Integrals.pdf 352.1 kB
  • 2. Combinatorics/20.2 Additional Exercises Combinatorics Solutions.pdf 251.6 kB
  • 2. Combinatorics/1.1 Section 2 Course Notes.pdf 231.5 kB
  • 2. Combinatorics/11.1 Combinations With Repetition.pdf 229.1 kB
  • 5. Tie-ins to Other Fields/1.2 Probability in Finance Solutions.pdf 188.9 kB
  • 4. Distributions/13.1 Poisson - Expected Value and Variance.pdf 149.5 kB
  • 4. Distributions/17.1 Normal Distribution - Expected Value and Variance.pdf 147.5 kB
  • 5. Tie-ins to Other Fields/1.1 Probability in Finance Homework.pdf 113.3 kB
  • 2. Combinatorics/20.1 Additional Exercises Combinatorics.pdf 109.1 kB
  • 2. Combinatorics/13.1 Symmetry Explained.pdf 87.1 kB
  • 3. Bayesian Inference/22.3 Bayesian Homework - Solutions.pdf 31.1 kB
  • 3. Bayesian Inference/22.2 Bayesian Homework .pdf 27.9 kB
  • 4. Distributions/29.2 Daily Views (post).xlsx 20.7 kB
  • 4. Distributions/29. Practical Example Distributions.srt 20.4 kB
  • 3. Bayesian Inference/22. Practical Example Bayesian Inference.srt 19.8 kB
  • 4. Distributions/29.5 Customers_Membership (post).xlsx 16.0 kB
  • 2. Combinatorics/20. Practical Example Combinatorics.srt 14.3 kB
  • 5. Tie-ins to Other Fields/1. Tie-ins to Finance.srt 10.1 kB
  • 4. Distributions/29.1 Customers_Membership.xlsx 9.9 kB
  • 4. Distributions/29.6 Daily Views.xlsx 9.8 kB
  • 4. Distributions/3. What are the two main types of distributions based on the type of data we have.srt 9.7 kB
  • 1. Introduction to Probability/2. What is the probability formula.srt 9.1 kB
  • 4. Distributions/15. What is a Continuous Distribution.srt 8.9 kB
  • 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.srt 8.6 kB
  • 4. Distributions/11. What is the Binomial Distribution.srt 8.5 kB
  • 4. Distributions/1. What is a probability distribution.srt 7.7 kB
  • 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.srt 7.4 kB
  • 1. Introduction to Probability/8. What is a complement.srt 6.9 kB
  • 1. Introduction to Probability/4. How to compute expected values.srt 6.8 kB
  • 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.srt 6.8 kB
  • 4. Distributions/13. What is the Poisson Distribution.srt 6.7 kB
  • 1. Introduction to Probability/6. What is a probability frequency distribution.srt 6.6 kB
  • 1. Introduction to Probability/1. What does the course cover.srt 5.8 kB
  • 2. Combinatorics/11. What are combinations and how are they similar to variations.srt 5.7 kB
  • 3. Bayesian Inference/7. What is the union of sets A and B.srt 5.7 kB
  • 4. Distributions/19. Standardizing a Normal Distribution.srt 5.4 kB
  • 3. Bayesian Inference/1. What is a set.srt 5.3 kB
  • 4. Distributions/27. What is the Logistic Distribution.srt 5.2 kB
  • 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).srt 5.1 kB
  • 4. Distributions/17. What is a Normal Distribution.srt 4.8 kB
  • 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.srt 4.7 kB
  • 2. Combinatorics/9. What if we couldn't use certain values more than once.srt 4.6 kB
  • 3. Bayesian Inference/3. What are the different ways two events can interact with one another.srt 4.5 kB
  • 2. Combinatorics/13. What is symmetry in Combinations.srt 4.4 kB
  • 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.srt 4.2 kB
  • 4. Distributions/25. What is an Exponential Distribution.srt 4.2 kB
  • 2. Combinatorics/3. When do we use Permutations.srt 4.2 kB
  • 4. Distributions/9. What is the Bernoulli Distribution.srt 3.9 kB
  • 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.srt 3.8 kB
  • 2. Combinatorics/19. A Summary of Combinatorics.srt 3.8 kB
  • 3. Bayesian Inference/15. Conditional Probability in Real-Life.srt 3.6 kB
  • 2. Combinatorics/7. Why can we use certain values more than once.srt 3.6 kB
  • 3. Bayesian Inference/11. What does it mean to for two events to be dependent.srt 3.5 kB
  • 2. Combinatorics/5. Solving Factorials.srt 3.3 kB
  • 4. Distributions/21. What is a Student's T Distribution.srt 2.9 kB
  • 4. Distributions/23. What is a Chi Squared Distribution.srt 2.8 kB
  • 3. Bayesian Inference/16. How do we apply the additive rule.srt 2.8 kB
  • 4. Distributions/7. What is the Discrete Uniform Distribution.srt 2.8 kB
  • 3. Bayesian Inference/9. Are all complements mutually exclusive.srt 2.6 kB
  • 3. Bayesian Inference/5. What is the intersection of sets A and B.srt 2.5 kB
  • 4. Distributions/5. Discrete Distributions and their characteristics..srt 2.5 kB
  • 2. Combinatorics/1. Why are combinatorics useful.srt 1.3 kB
  • 1. Introduction to Probability/3. What is the probability formula.html 154 Bytes
  • 1. Introduction to Probability/5. How to compute expected values.html 154 Bytes
  • 1. Introduction to Probability/7. What is a probability frequency distribution.html 154 Bytes
  • 1. Introduction to Probability/9. What is a complement.html 154 Bytes
  • 2. Combinatorics/10. Computing Variations without Repetition.html 154 Bytes
  • 2. Combinatorics/12. What are combinations and how are they similar to variations.html 154 Bytes
  • 2. Combinatorics/14. What is symmetry in Combinations.html 154 Bytes
  • 2. Combinatorics/16. How do we combine combinations of events with separate sample spaces.html 154 Bytes
  • 2. Combinatorics/18. What is the chance of winning the lottery.html 154 Bytes
  • 2. Combinatorics/2. Why are combinatorics useful.html 154 Bytes
  • 2. Combinatorics/4. When do we use Permutations.html 154 Bytes
  • 2. Combinatorics/6. Solving Factorials.html 154 Bytes
  • 2. Combinatorics/8. Why can we use certain values more than once.html 154 Bytes
  • 3. Bayesian Inference/10. Are all complements mutually exclusive.html 154 Bytes
  • 3. Bayesian Inference/12. What does it mean to for two events to be dependent.html 154 Bytes
  • 3. Bayesian Inference/14. What is the difference between P(AB) and P(BA).html 154 Bytes
  • 3. Bayesian Inference/17. How do we apply the additive rule.html 154 Bytes
  • 3. Bayesian Inference/19. How do we interpret the Multiplication Rule Formula.html 154 Bytes
  • 3. Bayesian Inference/2. What is a set.html 154 Bytes
  • 3. Bayesian Inference/21. Bayes' Theorem.html 154 Bytes
  • 3. Bayesian Inference/4. What are the different ways two events can interact with one another.html 154 Bytes
  • 3. Bayesian Inference/6. What is the intersection of sets A and B.html 154 Bytes
  • 3. Bayesian Inference/8. What is the union of sets A and B.html 154 Bytes
  • 4. Distributions/10. What is the Bernoulli Distribution.html 154 Bytes
  • 4. Distributions/12. What is the Binomial Distribution.html 154 Bytes
  • 4. Distributions/14. What is the Poisson Distribution.html 154 Bytes
  • 4. Distributions/16. What is a Continuous Distribution.html 154 Bytes
  • 4. Distributions/18. What is a Normal Distribution.html 154 Bytes
  • 4. Distributions/2. What is a probability distribution.html 154 Bytes
  • 4. Distributions/20. How do we Standardize a Normal Distribution.html 154 Bytes
  • 4. Distributions/22. What is a Student's T Distribution.html 154 Bytes
  • 4. Distributions/24. What is a Chi-Squared Distribution.html 154 Bytes
  • 4. Distributions/26. What is an Exponential Distribution.html 154 Bytes
  • 4. Distributions/28. What is a Logistic Distribution.html 154 Bytes
  • 4. Distributions/4. What are the two main types of distributions based on the type of data we have.html 154 Bytes
  • 4. Distributions/6. Discrete Distributions and Their Characteristics..html 154 Bytes
  • 4. Distributions/8. What is the Discrete Uniform Distribution.html 154 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes

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