Probability / Stats: The Foundations of Machine Learning

سرفصل های دوره

Real-world, code-oriented learning for programmers to use prob/stats in all of CS, Data Science and Machine Learning

1 - Diving in with code

1 - Code env setup and Python crash course

1 - prob-cs-notebooks.zip

1 - prob-cs-paper-notes.zip

2 - Getting started with code Feel of data

2 - prob-cs-notebooks.zip

2 - prob-cs-paper-notes.zip

3 - Foundations data types and representing data

4 - Practical note onehot vector encoding

5 - Exploring data types in code

6 - Central tendency mean median mode

7 - Section Review Tasks.html

2 - Measures of Spread

8 - Dispersion and spread in data variance standard deviation

9 - Dispersion exploration through code

10 - Section Review Tasks.html

3 - Applications and Rules for Probability

11 - Intro to uncertainty probability intuition

12 - Simulating coin flips for probability

13 - Conditional probability the most important concept in stats

14 - Applying conditional probability Bayes rule

15 - Application of Bayes rule in real world Spam detection

16 - Spam detection implementation issues

17 - Section Review Tasks.html

4 - Counting

18 - Rules for counting Mostly optional

19 - Section Review Tasks.html

5 - Random Variables Rationale and Applications

20 - Quantifying events random variables

21 - Two random variables joint probabilities

22 - Distributions rationale and importance

23 - Discrete distributions through code

24 - Continuous distributions probability densities

25 - Continuous distributions code

26 - Case study sleep analysis structure and code

27 - Section Review Tasks.html

6 - Visualization in Intuition Building

28 - Visualizing joint distributions the road to ML success

29 - Dependence and variance of two random variables

30 - Section Review Tasks.html

7 - Applications to the Real World

31 - Expected values decision making through probabilities

32 - Entropy The most important application of expected values

33 - Applying entropy coding decision trees for machine learning

34 - Foundations of Bayesian inference

35 - Bayesian inference code through PyMC3

36 - Section Review Tasks.html

8 - Extra Resources

37 - Bonus Lecture.html

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تولید کننده: Udemy-Training