| 4. Distributions/29. Practical Example Distributions.mp4 | 157.47 MB |
| 3. Bayesian Inference/22. Practical Example Bayesian Inference.mp4 | 144.96 MB |
| 2. Combinatorics/20. Practical Example Combinatorics.mp4 | 134.38 MB |
| 5. Tie-ins to Other Fields/1. Tie-ins to Finance.mp4 | 98.83 MB |
| 4. Distributions/3. What are the two main types of distributions based on the type of data we have.mp4 | 91.61 MB |
| 1. Introduction to Probability/2. What is the probability formula.mp4 | 85.88 MB |
| 4. Distributions/15. What is a Continuous Distribution.mp4 | 84.16 MB |
| 5. Tie-ins to Other Fields/2. Tie-ins to Statistics.mp4 | 77.19 MB |
| 1. Introduction to Probability/4. How to compute expected values.mp4 | 75.74 MB |
| 4. Distributions/1. What is a probability distribution.mp4 | 73.36 MB |
| 4. Distributions/11. What is the Binomial Distribution.mp4 | 68.89 MB |
| 5. Tie-ins to Other Fields/3. Tie-ins to Data Science.mp4 | 63.46 MB |
| 1. Introduction to Probability/6. What is a probability frequency distribution.mp4 | 61.61 MB |
| 1. Introduction to Probability/8. What is a complement.mp4 | 59.11 MB |
| 2. Combinatorics/11. What are combinations and how are they similar to variations.mp4 | 57.3 MB |
| 3. Bayesian Inference/7. What is the union of sets A and B.mp4 | 57.21 MB |
| 4. Distributions/13. What is the Poisson Distribution.mp4 | 55.8 MB |
| 1. Introduction to Probability/1. What does the course cover.mp4 | 52.68 MB |
| 3. Bayesian Inference/20. When do we use Bayes' Theorem in Real Life.mp4 | 50 MB |
| 4. Distributions/27. What is the Logistic Distribution.mp4 | 49.98 MB |
| 3. Bayesian Inference/18. How do we derive the Multiplication Rule formula.mp4 | 49.06 MB |
| 4. Distributions/19. Standardizing a Normal Distribution.mp4 | 47.92 MB |
| 3. Bayesian Inference/3. What are the different ways two events can interact with one another.mp4 | 47.44 MB |
| 3. Bayesian Inference/13. What is the difference between P(AB) and P(BA).mp4 | 45.84 MB |
| 3. Bayesian Inference/1. What is a set.mp4 | 45.52 MB |
| 4. Distributions/17. What is a Normal Distribution.mp4 | 43.75 MB |
| 2. Combinatorics/9. What if we couldn't use certain values more than once.mp4 | 43.08 MB |
| 2. Combinatorics/3. When do we use Permutations.mp4 | 41.49 MB |
| 2. Combinatorics/17. What is the chance of a single ticket winning the lottery.mp4 | 41.32 MB |
| 2. Combinatorics/13. What is symmetry in Combinations.mp4 | 40.27 MB |
| 4. Distributions/25. What is an Exponential Distribution.mp4 | 40.17 MB |
| 2. Combinatorics/19. A Summary of Combinatorics.mp4 | 38.31 MB |
| 2. Combinatorics/5. Solving Factorials.mp4 | 36.13 MB |
| 3. Bayesian Inference/15. Conditional Probability in Real-Life.mp4 | 34.91 MB |
| 3. Bayesian Inference/11. What does it mean to for two events to be dependent.mp4 | 34.78 MB |
| 4. Distributions/9. What is the Bernoulli Distribution.mp4 | 34.15 MB |
| 2. Combinatorics/7. Why can we use certain values more than once.mp4 | 33.97 MB |
| 2. Combinatorics/15. How do we combine combinations of events with separate sample spaces.mp4 | 33.04 MB |
| 3. Bayesian Inference/16. How do we apply the additive rule.mp4 | 26.95 MB |
| 3. Bayesian Inference/5. What is the intersection of sets A and B.mp4 | 26.93 MB |
| 4. Distributions/23. What is a Chi Squared Distribution.mp4 | 26.37 MB |
| 3. Bayesian Inference/9. Are all complements mutually exclusive.mp4 | 25.4 MB |
| 4. Distributions/7. What is the Discrete Uniform Distribution.mp4 | 24.4 MB |
| 4. Distributions/5. Discrete Distributions and their characteristics..mp4 | 22.68 MB |
| 4. Distributions/21. What is a Student's T Distribution.mp4 | 22 MB |
| 2. Combinatorics/1. Why are combinatorics useful.mp4 | 16.17 MB |
| 4. Distributions/29.3 FIFA19 (post).csv | 8.65 MB |
| 4. Distributions/29.4 FIFA19.csv | 8.65 MB |
| 3. Bayesian Inference/22.1 CDS_2017-2018 Hamilton.pdf | 845.31 KB |
| 4. Distributions/1.1 Course Notes - Probability Distributions.pdf | 448.06 KB |