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Machine Learning Magic: Unleashing the Power of Algorithms
Mammoth interactive Course Introduction
Preface
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
Course Overview - Machine Learning Fundamentals
Course Overview (13:46)
Source Files
(Prerequisite) Introduction to Machine Learning
What Is Machine Learning (5:26)
Types Of Machine Learning Models (12:17)
What Is Supervised Learning (11:04)
What Is Unsupervised Learning (8:17)
How Does A Machine Learning Agent Learn (7:38)
What Is Inductive Learning (4:11)
Performance Of A Machine Learning Algorithm (4:14)
Handle Noise In Data (5:22)
Powerful Tools With Machine Learning Libraries- (12:11)
(Prerequisite) Introduction to Python
Introduction (4:42)
Variables (19:17)
Type Conversion Examples (10:04)
Operators (7:04)
Operators Examples (21:52)
Collections (8:23)
Lists (11:38)
Multidimensional List Examples (8:05)
Tuples Examples (8:34)
Dictionaries Examples (14:24)
Ranges Examples (8:30)
Conditionals (6:41)
If Statement Examples (10:16)
If Statement Variants Examples (11:18)
Loops (7:00)
While Loops Examples (11:30)
For Loops Examples (11:18)
Functions (7:47)
Functions Examples (9:16)
Parameters And Return Values Examples (13:46)
Classes And Objects (11:13)
Classes Example (13:11)
Objects Examples (9:54)
Inheritance Examples (17:26)
Static Members Example (11:03)
Summary And Outro (4:06)
Source code
Probability and Statistics for Machine Learning
Probability And Information Theory Overview (5:15)
Combinatorics For Probability (8:44)
Law Of Large Numbers (10:38)
Calculate Center Of Distribution (7:40)
Distributions in Machine Learning
Uniform Distribution (5:25)
Gaussian Distribution (3:45)
Log-Normal Distribution (3:28)
Exponential Distribution (3:04)
Laplace Distribution (1:54)
Binomial Distribution (9:05)
Multinomial Distribution (3:59)
Poisson Distribution (4:21)
Machine Learning Optimization
Calculate Error Of Machine Learning Model (8:44)
Source Files
Source Files
Handle Noise In Data
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