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Machine Learning Fundamentals
00b Course Overview - Machine Learning Fundamentals
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00 Course Overview (13:46)
(Prerequisite) Introduction to Machine Learning
00B Types Of Machine Learning Models (12:17)
00A What Is Machine Learning (5:26)
00C What Is Supervised Learning (11:04)
00D What Is Unsupervised Learning (8:17)
01 How Does A Machine Learning Agent Learn (7:38)
02 What Is Inductive Learning (4:11)
03 Performance Of A Machine Learning Algorithm (4:14)
04 Handle Noise In Data (5:22)
05 Powerful Tools With Machine Learning Libraries- (12:11)
(Prerequisite) Introduction to Python
00. Introduction (4:42)
01. Intro To Python (5:46)
01.01 What Is Google Colab (4:24)
01.02 What If I Get Errors (2:40)
01.03 How Do I Terminate A Session (2:40)
02. Variables (19:17)
02b. Variables Examples-33 (10:42)
03. Type Conversion Examples (10:04)
04. Operators (7:04)
05. Operators Examples (21:52)
06. Collections (8:23)
07. Lists (11:38)
08. Multidimensional List Examples (8:05)
09. Tuples Examples (8:34)
10. Dictionaries Examples (14:24)
11. Ranges Examples (8:30)
12. Conditionals (6:41)
13. If Statement Examples (10:16)
14. If Statement Variants Examples (11:18)
15. Loops (7:00)
16. While Loops Examples (11:30)
17. For Loops Examples (11:18)
18. Functions (7:47)
19. Functions Examples (9:16)
20. Parameters And Return Values Examples (13:46)
21. Classes And Objects (11:13)
22. Classes Example (13:11)
23. Objects Examples (9:54)
24. Inheritance Examples (17:26)
25. Static Members Example (11:03)
26. Summary And Outro (4:06)
03 Probability and Statistics for Machine Learning
03-01 Probability And Information Theory Overview (5:15)
03-02 Combinatorics For Probability (8:44)
03-03 Law Of Large Numbers (10:38)
03-04 Calculate Center Of Distribution (7:40)
04 Distributions in Machine Learning
04-01 Uniform Distribution (5:25)
04-02 Gaussian Distribution (3:45)
04-03 Log-Normal Distribution (3:28)
04-04 Exponential Distribution (3:04)
04-05 Laplace Distribution (1:54)
04-06 Binomial Distribution (9:05)
04-07 Multinomial Distribution (3:59)
04-08 Poisson Distribution (4:21)
05 Machine Learning Optimization
05 Calculate Error Of Machine Learning Model (8:44)
03-03 Law Of Large Numbers
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