Statistics and Probability with Python Explained for Beginners

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Course curriculum

1. Introduction to Instructor and AISciences
2. Introduction To Instructor
3. Focus of the Course
1. Probability vs Statistics
1. Definition of Set
2. Definition of Set Exercise 01
3. Definition of Set Solution 01
4. Definition of Set Exercise 02
5. Definition of Set Solution 02
6. Cardinality of a Set
7. Subsets PowerSet UniversalSet
8. Python Practice Subsets
9. PowerSets Solution
10. Operations
11. Operations Exercise 01
12. Operations Solution 01
13. Operations Exercise 02
14. Operations Solution 02
15. Operations Exercise 03
16. Operations Solution 03
17. Python Practice Operations
18. VennDiagrams Operations
19. Homework
1. Random Experiment
2. Outcome and Sample Space
3. Outcome and Sample Space Exercise 01
4. Outcome and Sample Space Solution 01
5. Event
6. Event Exercise 01
7. Event Solution 01
8. Event Exercise 02
9. Event Solution 02
10. Recap and Homework
1. Probability Model
2. Probability Axioms
3. Probability Axioms Derivations
4. Probability Axioms Derivations Exercise 01
5. Probability Axioms Derivations Solution 01
6. Probablility Models Example
7. Probablility Models More Examples
8. Probablility Models Continous
9. Conditional Probability
10. Conditional Probability Example
11. Conditional Probability Formula
12. Conditional Probability in Machine Learning
13. Conditional Probability Total Probability Theorem
14. Probablility Models Independence
15. Probablility Models Conditional Independence
16. Probablility Models Conditional Independence Exercise 01
17. Probablility Models Conditional Independence Solution 01
18. Probablility Models BayesRule
19. Probablility Models towards Random Variables
20. HomeWork
1. Introduction
2. Random Variables Examples
3. Random Variables Examples Exercise 01
4. Random Variables Examples Solution 01
5. Bernulli Random Variables
6. Bernulli Trail Python Practice
7. Bernulli Trail Python Practice Exercise 01
8. Bernulli Trail Python Practice Solution 01
9. Geometric Random Variable
10. Geometric Random Variable Normalization Proof Optional
11. Geometric Random Variable Python Practice
12. Binomial Random Variables
13. Binomial Python Practice
14. Random Variables in Real DataSets
15. Random Variables in Real DataSets Exercise 01
16. Random Variables in Real DataSets Solution 01
17. Homework
1. Zero Probability to Individual Values
2. Zero Probability to Individual Values Exercise 01
3. Zero Probability to Individual Values Solution 01
4. Probability Density Functions
5. Probability Density Functions Exercise 01
6. Probability Density Functions Solution 01
7. Uniform Distribution
8. Uniform Distribution Exercise 01
9. Uniform Distribution Solution 01
10. Uniform Distribution Python
11. Exponential
12. Exponential Exercise 01
13. Exponential Solution 01
14. Exponential Python
15. Distributions Introduction using Scipy Module Python
16. Continuous Distributions using Scipy Module Python
17. Data Preparation for Sleep Analysis
18. Sleep Analysis Project
19. Gaussian Random Variables
20. Gaussian Random Variables Exercise 01
21. Gaussian Random Variables Solution 01
22. Gaussian Python
23. Transformation of Random Variables
24. Homework
1. Definition
2. Sample Mean
3. Law of Large Numbers
4. Law of Large Numbers Famous Distributions
5. Law of Large Numbers Famous Distributions Python
6. Variance
7. Homework
8. Data Introduction
9. Preprocessing
10. Tokenization and Stemming
11. Making a Dictionary
12. Basic Stats
13. Probability Calculation
1. Project Bayes Classifier From Scratch
1. Joint Distributions
2. Joint Distributions Exercise 01
3. Joint Distributions Solution 01
4. Joint Distributions Exercise 02
5. Joint Distributions Solution 02
6. Joint Distributions Exercise 03
7. Joint Distributions Solution 03
8. Multivariate Gaussian
9. Conditioning Independence
10. Classification
11. Naive Bayes Classification
12. Regression
13. Curse of Dimensionality
14. Homework
1. Parametric Distributions
2. MLE
3. LogLiklihood
4. MAP
5. Logistic Regression
6. Ridge Regression
7. DNN
1. Permutations
2. Combinations
3. Binomial Random Variable
4. Logistic Regression Formulation
5. Logistic Regression Derivation

About this course

$199.99
134 lessons
15 hours of video content

Statistics and Probability with Python Explained for Beginners

Course curriculum

Introduction to Course

Probability vs Statistics

Sets

Experiment

Probability Model

Random Variables

Continous Random Variables

Expectations

Project Bayes Classifier

Multiple Random Variables

Optional Estimation

Mathematical Derivations for Math Lovers (Optional)

About this course