English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 48m | 395 MB
Core mathematical concepts such as single-variable calculus, multivariable calculus, matrices, and linear algebra are the underpinnings of all machine learning algorithms. And for many professionals with an interest in machine learning and AI, revisiting these concepts can be a bit intimidating. This course demystifies the essential math that you need to grasp—and implement—in order to write machine learning algorithms in Python. Review fundamental algebraic concepts; derivatives and optimization; statistics; and the basics of probability.
Table of Contents
Introduction
1 Preparing for the labs
Equations, Graphs, and Functions
2 Getting started with equations
3 Introduction to linear equations
4 Intercepts and slope
5 Systems of equations
6 Exponentials, radicals, and logarithms
7 Polynomials
8 Polynomial operations
9 Factorization
10 Factoring squares
11 Introduction to quadratic equations
12 Functions
Derivatives and Optimization
13 Rates of change
14 Introduction to limits
15 Finding limits
16 Derivative rules and operations
17 Using derivatives to analyze functions
18 Second-order derivatives
19 Optimizing functions
20 Multivariate differentiation
21 Introduction to integration
Vectors and Matrices
22 Introduction to vectors
23 Vector multiplication
24 Introduction to matrices
25 Matrix multiplication
26 Matrix division
27 Solving systems of equations with matrices
28 Eigenvalues and eigenvectors
Statistics and Probability
29 Data
30 Visualizing data
31 Measures of central tendency
32 Measures of variance
33 Comparing data
34 Probability basics
35 Conditional probability and dependence
36 Binomial variables and distributions
37 Sample and sampling distributions
38 Confidence intervals
39 Hypothesis testing
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