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Statistical and Probability foundations for Machine Learning: Learning Statistics, Probability and Bayes Classifier In today’s ultra-competitive business universe, Probability and Statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance. But why do you need to master probability and statistics in Python? The answer is an expert grip on the concepts of Statistics and Probability with Data Science will enable you to take your career to the next level. The course ‘Mastering Probability and Statistics in Python’ is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regards to Python. The course is:- Easy to understand.
- Expressive.
- Comprehensive.
- Practical with live coding.
- About establishing links between Probability and Machine Learning.

- The importance of Statistics and Probability in Data Science.
- The foundations for Machine Learning and its roots in Probability Theory.
- The important concepts from the absolute beginning with comprehensive unfolding with examples in Python.
- Practical explanation and live coding with Python.
- Probabilistic view of modern Machine Learning.
- Implementation of Bayes classifier (Machine Learning Model) on a real dataset with basic and simple concepts of probability and statistics.

**+ Table of Contents**

**Introduction to Course**

1 Introduction to Instructor and AISciences

2 Focus of the Course

3 Request for Your Honest Review

4 Link to Github to get the Python Notebooks

**Probability vs Statistics**

5 Probability vs Statistics

**Sets**

6 Definition of Set

7 Cardinality of a Set

8 Subsets PowerSet UniversalSet

9 Python Practice Subsets

10 PowerSets Solution

11 Operations

12 Python Practice Operations

13 VennDiagrams Operations

14 Homework

**Experiment**

15 Random Experiment

16 Outcome and Sample Space

17 Event

18 Recap and Homework

**Probability Model**

19 Probability Model

20 Probability Axioms

21 Probability Axioms Derivations

22 Probablility Models Example

23 Probablility Models More Examples

24 Probablility Models Continous

25 Conditional Probability

26 Conditional Probability Example

27 Conditional Probability Formula

28 Conditional Probability in Machine Learning

29 Conditional Probability Total Probability Theorem

30 Probablility Models Independence

31 Probablility Models Conditional Independence

32 Probablility Models BayesRule

33 Probablility Models towards Random Variables

34 HomeWork

**Random Variables**

35 Introduction

36 Random Variables Examples

37 Bernulli Random Variables

38 Bernulli Trail Python Practice

39 Geometric Random Variable

40 Geometric Random Variable Normalization Proof Optional

41 Geometric Random Variable Python Practice

42 Binomial Random Variables

43 Binomial Python Practice

44 Random Variables in Real DataSets

45 Homework

**Continous Random Variables**

46 Zero Probability to Individual Values

47 Probability Density Functions

48 Uniform Distribution

49 Uniform Distribution Python

50 Exponential

51 Exponential Python

52 Gaussian Random Variables

53 Gaussian Python

54 Transformation of Random Variables

55 Homework

**Expectations**

56 Definition

57 Sample Mean

58 Law of Large Numbers

59 Law of Large Numbers Famous Distributions

60 Law of Large Numbers Famous Distributions Python

61 Variance

62 Homework

**Project Bayes Classifier**

63 Project Bayes Classifier From Scratch

**Multiple Random Variables**

64 Joint Distributions

65 Multivariate Gaussian

66 Conditioning Independence

67 Classification

68 Naive Bayes Classification

69 Regression

70 Curse of Dimensionality

71 Homework

**Optional Estimation**

72 Parametric Distributions

73 MLE

74 LogLiklihood

75 MAP

76 Logistic Regression

77 Ridge Regression

78 DNN

**Mathematical Derivations for Math Lovers (Optional)**

79 Permutations

80 Combinations

81 Binomial Random Variable

82 Logistic Regression Formulation

83 Logistic Regression Derivation

84 THANK YOU Bonus Video

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